# Reconstruction-based approach for chest X-ray image segmentation and enhanced multi-label chest disease classification

Aya Hage Chehade, Nassib Abdallah, Jean-Marie Marion, Mathieu Hatt, Mohamad Oueidat, Pierre Chauvet

► To cite this version:

Aya Hage Chehade, Nassib Abdallah, Jean-Marie Marion, Mathieu Hatt, Mohamad Oueidat, et al. Reconstruction-based approach for chest X-ray image segmentation and enhanced multi-label chest disease classification. Artificial Intelligence in Medicine, 2025, 165, pp.103135. 10.1016/j.artmed.2025.103135. hal-05287821

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Research paper

# Reconstruction-based approach for chest X-ray image segmentation and enhanced multi-label chest disease classification

Aya Hage Chehade a,*, Nassib Abdallah a,b, Jean-Marie Marion a, Mathieu Hatt b,
Mohamad Oueidat c, Pierre Chauvet a

<figure><img src="image_3.png" /></figure>

## ARTICLE INFO

### Keywords:

ChestX-ray14 dataset

Segmentation

CycleGAN

Machine learning

Feature engineering

Radiomics

Image processing

Image classification

## ABSTRACT

U-Net is a commonly used model for medical image segmentation. However, when applied to chest X-ray images that show pathologies, it often fails to include these critical pathological areas in the generated masks. To address this limitation, in our study, we tackled the challenge of precise segmentation and mask generation by developing a novel approach, using CycleGAN, that encompasses the areas affected by pathologies within the region of interest, allowing the extraction of relevant radiomic features linked to pathologies. Furthermore, we adopted a feature selection approach to focus the analysis on the most significant features. The results of our proposed pipeline are promising, with an average accuracy of 92.05% and an average AUC of 89.48% for the multi-label classification of effusion and infiltration acquired from the ChestX-ray14 dataset, using the XGBoost model. Furthermore, applying our methodology to the classification of the 14 diseases in the ChestX-ray14 dataset resulted in an average AUC of 83.12%, outperforming previous studies. This research highlights the importance of effective pathological mask generation and features selection for accurate classification of chest diseases. The promising results of our approach underscore its potential for broader applications in the classification of chest diseases.

## 1. Introduction

Lung diseases significantly impact global health, standing as the third leading cause of death worldwide [1] and accounting for around five million deaths each year [2]. Therefore, early diagnosis is crucial to mitigate the life-threatening implications of lung diseases and to improve the chance of recovery and survival for individuals already affected, by enabling prompt and effective treatment. Timely detection and management of lung diseases is essential, not only to arrest disease progression but also to reduce possible complications.

Imaging plays a vital role in the diagnosis of lung diseases, providing diverse techniques for comprehensive evaluation and guiding appropriate treatment strategies. Medical imaging modalities such as Chest X-ray (CXR), Computed Tomography (CT) scans, and Magnetic Resonance Imaging (MRI) serve as essential diagnostic tools, allowing visualization of the lung and the identification of abnormalities [3]. Chest radiography, characterized by its simplicity, accessibility, non-invasiveness and cost-effectiveness, remains one of the most widely used radiological methods for screening, diagnosing various chest diseases [4,5], assessing disease severity, and identifying potential complications [6,7].

However, manual examination of CXR images is both time-intensive and challenging for radiologists, given the complex nature of chest X-rays [8]. Chest X-ray images often encompass multiple potential diseases, making the classification of these images a multi-label problem. Identifying various types of disease in chest X-ray images poses a significant challenge for experts, due to the vast number of potential lung diseases, many of which share similar visual characteristics. This complexity can result in errors of interpretation, missed diagnoses, and potentially critical diagnostic errors. This underscores the importance of adopting advanced methods for the accurate interpretation and diagnosis of these images.

Recently, interest in Artificial Intelligence (AI) technologies has seen a marked increase, driven by their capabilities to expedite data analysis, improve medical imaging diagnosis, boost the efficiency of Chest X-ray evaluations for lung diseases classification, and aid in decision-making [8]. Through automated analysis of pathological images, Computer-Aided Diagnosis (CAD) systems have the potential to significantly reduce the workload of radiologists and improve the precision of clinical diagnosis [9,10]. Machine learning (ML) and deep

<footnote>a LARIS, University of Angers, France</footnote>

<footnote>b LaTIM, INSERM UMR 1101, University of Brest, France</footnote>

<footnote>c Faculty of Technology, Lebanese University, Lebanon</footnote>

<footnote>* Corresponding author.
E-mail address: aya.hagechehade@etud.univ-angers.fr (A. Hage Chehade).</footnote>
learning (DL) can significantly improve the accuracy of clinical di-
agnoses [11]. In the last few years, DL has garnered extensive at-
tention for its broad applicability in solving problems that require
automated features extraction and classification. Deep learning-based
algorithms have demonstrated significant success and effectiveness in
a wide range of computer vision tasks, including image classifica-
tion [12], disease detection [13,14], medical diagnosis [15,16] and
healthcare analysis [17].

In clinical practice, radiologists use pattern recognition in medical images for diagnostic decision-making. Skills and knowledge of radiologists can be leveraged through radiomics, which has proven the effectiveness of imaging-based biomarkers for cancer staging and prognosis [18]. Indeed, radiomics are an advancing field within quantitative image analysis, focused on connecting extensive image data mining with clinical and biological outcomes [19]. Through the extraction of radiomic features, it is possible to precisely quantify pathology-specific characteristics and assess its progression from a series of medical images. This method relies on advanced image analysis algorithms, transforming images into mineable high-dimensional data [20]. Radiomics enables the extraction of relevant information from images, providing comprehensive analysis and evaluation of the two-dimensional landscape in the region of interest (ROI) [21]. The core concept of radiomics lies in the understanding that medical images contain a wealth of information far beyond what can be perceived by the human eye. Quantitative imaging features, also known as “radiomic features”, offer a deeper insight into the characteristics of tumor phenotypes, including aspects such as intensity, shape, size or volume, and texture [22]. These radiomic features can be extracted using various imaging modalities, providing a comprehensive understanding of the pathology’s behavior and properties. By leveraging these detailed radiomic features, physicians can gain a more nuanced understanding of the biological and clinical significance of pathologies, enabling more precise diagnostics, prognostics, and personalized treatment strategies. Thus, radiomics plays a crucial role in precision medicine, supporting clinical decision-making with concrete evidence.

Although radiomics has considerable potential to improve the pre-
cision of clinical diagnostics, its exploration remains relatively under-
explored, particularly in light of the dominance of DL in previous stud-
ies. This highlights a notably promising research area into the applica-
tion of radiomics for chest diseases classification, offering substantial
opportunities to boost diagnostic precision.

Effective radiomic analysis requires accurate segmentation and gen-
eration of masks that delineate the regions of interest. Traditional
methods like U-Net, while widely used model for medical image seg-
mentation, often fail to include specific disease-affected regions within
the masks, which are crucial for accurate radiomic analysis. The objec-
tive of this study is to address the issue of disease affected areas being
excluded from masks, ensuring that these critical regions are accurately
represented to enhance diagnostic accuracy.

Our work's novelty is rooted in the development of a novel pathol-
ogy mask generation approach that precisely targets areas affected
by pathology within the region of interest (ROI). This approach al-
lows for the precise extraction of radiomic features by ensuring that
the features are directly derived from the areas most indicative of
disease. Additionally, we implemented a novel features selection ap-
proach designed to select the most pertinent and relevant features for
each specific pathological conditions even when multiple diseases are
present in the same image. This strategy concentrates our analysis on
the most influential features, significantly improving the classification
model's efficiency. These advancements in pathology mask generation
and features selection collectively represent a significant stride towards
refining machine learning models for more precise and effective disease
classification.

Using the NIH ChestX-ray14 dataset, the experiment results demon-
strate that by extracting radiomic features from ROIs delineated by
our proposed segmentation approach for pathology mask generation,

followed by the selection of the most significant features, machine
learning models achieve remarkable performance in the multi-label
classification of two diseases, namely effusion and infiltration. More-
over, when extending our methodology to encompass the multi-label
classification of all 14 diseases present in the ChestX-ray14 dataset,
our strategy demonstrates superior performance, outperforming the
state-of-the-art results. This advancement highlights the effectiveness
and potential of our approach in enhancing the precision of chest
disease classification from Chest X-ray images, marking a significant
step forward in the field of medical imaging analysis.

Our contributions are summarized as follows:

* We proposed a novel segmentation approach based on CycleGAN model to generate pathology masks, tailored to include areas impacted by pathologies within the ROI. This approach significantly improves disease classification outcomes by enabling the extraction of radiomic features from areas specifically indicative of pathological conditions.

* We proposed a novel features selection approach to identify the most relevant features, and we assessed the impact of features selection on the multi-label classification of chest diseases.

* We conducted experiments on the ChestX-ray14 dataset and the experiment results demonstrated that our methodology yields superior performance compared to the state-of-the-art approaches.

## 2. Related work

With the emergence of DL and the release of the large-scale CXR dataset [23], many researchers have concentrated on developing DL-based methods for chest disease classification. Moreover, transfer learning, involving the use of pre-trained models, was employed as a strategy to address the challenges associated with the initial training of deep neural networks from scratch.

Indeed, Mann et al. [24] utilized three convolutional neural networks: DenseNet-121, ResNet-50, and EfficientNetB1. DenseNet-121 demonstrated the highest performance among the models. It was used as a feature extractor, with an enhanced fully connected layer added and trained, while the core architecture of DenseNet-121 remained unaltered. Souid et al. [25] proposed the classification of lung diseases in frontal chest X-rays using the MobileNet V2 model, supplemented by CNN layers. Their approach obtained an AUC of 81% for the classification of the 14 diseases in the ChestX-ray14 dataset.

Some studies have explored the use of attention mechanisms. For example, Wang et al. [26] utilized the pre-trained DenseNet-121 as a backbone for feature extraction, and integrates three attention modules (channel-wise, element-wise, and scale-wise) into a unified framework. The channel-wise module emphasizes discriminative feature channels, the element-wise module focuses on regions with pathological abnormalities, and the scale-wise module recalibrates feature maps across different scales. Their model achieved an average AUC of 82.6% for the classification of the 14 diseases in the ChestX-ray-14 dataset. Guan et al. proposed a category-wise residual attention learning (CRAL) framework for chest disease classification from the ChestX-ray14 dataset [27]. The CRAL framework is composed of two modules: a feature integration module and an attention module. The feature integration module extracts high-level features from images using a convolutional neural network such as DenseNet-121. These extracted features are then fed into the attention module, which learns category-based attention scores to assign weights to different spatial positions in the feature maps. Their method achieved an average AUC score of 81.6%. Ma et al. [28] proposed a cross-attention network scheme for thoracic disease classification, using an attention loss that forced the model to focus more accurately on pathogenic areas, and a multi-label balance loss that helped the model overcome the class imbalance problem. The model achieved an average AUC of 81.7%. Chen et al. [29] proposed a dual attention network that incorporates a Channel Attention Module (CAM)
and a Spatial Attention Module (SAM), designed to selectively enhance features highly related to lesion area. Their model reached a mean AUC of 81.86% for the classification of all diseases in the ChestX-ray14 dataset.

Jiang et al. introduced TransDD, a Transformer-based dual-path decoder that incorporates a learnable label embedding to better associate local discriminative features with their corresponding classes [30]. It consists of three key components: spatial reduction attention to handle variations in lesion appearance and location, dual-path attention to explicitly connect features and labels, and a feature enhancement module to balance classification scores based on feature and label outputs. TransDD achieved a mean AUC of 83.1% across 14 classes on the ChestX-ray14 dataset.

To improve multi-label classification of chest diseases, Yang et al. [31] implemented a triple network ensemble learning framework (TNELF) that includes three convolutional neural networks: Efficient-NB4, DenseNet-169 and Resnext-50, and a noisy label detection method using binary cross-entropy and class-balanced sigmoid cross-entropy Loss to mitigate the impact of noisy labels. Random horizontal flip was performed for data augmentation. The proposed framework obtained an average AUC of 82.7% for the classification of all diseases in the ChestX-ray-14 dataset.

To tackle the issue of performance degradation in deep learning models for chest X-ray analysis caused by noisy labels, Wang et al. [32] proposed the Stochastic Neural Ensemble Learning (SNEL) framework. This framework incorporates a fast neural ensemble method that collects parameters from both model instances and optimization trajectories. Additionally, the authors design a noise-robust loss function that optimizes model performance while ensuring diversity within the ensemble. The results demonstrated the framework's effectiveness in learning from noisy labels. To address the challenge of performance degradation in deep learning models for chest X-ray analysis due to domain shifts, such as variations in scanner vendors, imaging protocols, and patient demographics, Wang et al. [33] introduced the Domain-Ensemble Learning with Cross-Domain Mixup (DELCOM) method. This approach trains multiple domain-specific models using both source domain data and virtual samples generated through a cross-domain mixup technique which combines data from different source domains. Their approach surpassed state-of-the-art domain generalization techniques and achieved an average AUC of 80.16% across 8 classes from the ChestX-ray14 dataset with DenseNet-121 as the backbone.

# 3. Material and methods

## 3.1. Dataset

ChestX-ray14 dataset [23], also known as NIH Chest X-ray Dataset, is a large collection of chest X-ray images released by hospitals affiliated to National Institutes of Health Clinical Center in 2017, and includes 112,120 frontal-view chest radiography images from 30,805 unique patients. All CXRs are 1024 × 1024 in size and are in PNG format. The dataset also contains other data such as the gender and age of the patient and the view position of the X-ray (i.e. whether the images were captured in the posterior-anterior (PA) or anterior-posterior (AP) position). Each CXR in the dataset is annotated with one or more labels from a set of 14 chest diseases. As illustrated in Fig. 1, out of the total 112,120 CXR images, 60,361 are classified as "No finding", indicating that none of these diseases were detected. The remaining 51,759 CXR images are labeled with information for 14 different types of chest diseases. Of these, 30,963 CXR images are labeled with a single pathology, offering insights into the distinct radiographic features of individual chest conditions. The 20,796 CXR images labeled with two or more diseases are particularly valuable for understanding the complexities and co-occurrences of multiple diseases.

Table 1 details the number of images labeled by each pathology in the ChestX-ray14 dataset. The first line displays the total image

count for each disease, providing an overview of the prevalence of each pathology within the dataset. The third line presents the number of images that are labeled with a specific single pathology.

The second line shows the number of images labeled with two or more diseases which involved 820 types of pathology combinations within the dataset. The range in the number of images for each type of pathology varies significantly, extending from several thousand to just a few. For example, there are 1,604 images labeled by both infiltration and effusion; 1,350 images labeled by both infiltration and atelectasis; 737 images labeled by infiltration, atelectasis and effusion; 441 images labeled by both infiltration and consolidation; 81 images labeled by infiltration, atelectasis, consolidation and effusion. This variation not only illustrates the prevalence of certain conditions but also highlights the rarity and complexity of others.

For the multi-label classification task of effusion and infiltration, we have specifically selected chest X-ray images labeled as both effusion and infiltration in the ChestX-ray14 dataset, amounting to a total of 1,604 images. Additionally, to maintain a balanced approach, an equal number of CXRs individually labeled either as effusion or as infiltration are also used in our study. Therefore, we conducted our experiments on a total of 4,812 CXR images, with 1,604 images dedicated to each specific category. We chose these two diseases (effusion and infiltration) because they have the highest number of images containing both conditions simultaneously compared to other diseases in the dataset. Thereafter, we applied our methodology to the classification of the 14 diseases of the ChestX-ray14 dataset.

## 3.2. Overview of the methodology

Fig. 2 shows an overview of the methodology used to perform the multi-label classification of lung diseases on the basis of chest X-ray images. The CXR images of the two lung diseases, namely effusion and infiltration were fed into the system. 4,812 images were randomly selected and used in all stages of our study, 1,604 images for each specific category: images exclusively labeled as effusion, images solely identified with infiltration, and images jointly marked for effusion and infiltration. To generate precise masks of diseases present in radiographic images, we developed a novel segmentation method. This method starts with the application of the CycleGAN model to radiographic images to generate a healthy restoration of the lungs. The next step involves performing an image subtraction between the restored version, considered pseudo-healthy, and the original image presenting pathologies. This process creates a pathology map that accurately highlights the regions affected by anomalies in the initial image. Finally, this map is converted into a binary format, thus producing a detailed mask of the pathologies. The images were then resized to 224 × 224 pixels, transformed to grayscale and the radiomic features were extracted. To select the most efficient features, we adopted a new features selection approach that initially incorporates Hierarchical Cluster Analysis (HCA), followed by a rigorous evaluation using a Test\_value. Following the features selection process, machine learning algorithm was deployed for image classification based on the selected features. The data was partitioned into training and testing sets, with 80% allocated for training and the remaining 20% designated as test data (randomly chosen). The machine learning algorithm is trained using the images features of the training set. Image features of the testing set are used to assess the performance of the model.

## 3.3. Mask generation

In this section, our goal is to develop a method to create accurate masks, aiming to isolate the lungs as well as the most indicative regions of pathologies. We will begin by introducing the application of the U-Net model for generating these masks, and then we will present our novel approach.
<table><caption>**Table 1**
Number of images categorized by pathology in the ChestX-ray14 dataset.</caption><thead><tr><th>Pathology</th><th>Infiltration</th><th>Effusion</th><th>Atelectasis</th><th>Nodule</th><th>Mass</th><th>Pneumothorax</th><th>Consolidation</th></tr></thead><tbody><tr><td>Image number</td><td>19,894</td><td>13,317</td><td>11,559</td><td>6331</td><td>5782</td><td>5302</td><td>4667</td></tr><tr><td>Multi-pathology image number</td><td>10,347</td><td>9362</td><td>7344</td><td>3626</td><td>3643</td><td>3108</td><td>3357</td></tr><tr><td>Single pathology image number</td><td>9547</td><td>3955</td><td>4215</td><td>2705</td><td>2139</td><td>2194</td><td>1310</td></tr><tr><th>Pathology</th><th>Pleural_Thickening</th><th>Cardiomegaly</th><th>Emphysema</th><th>Edema</th><th>Fibrosis</th><th>Pneumonia</th><th>Hernia</th></tr><tr><td>Image number</td><td>3385</td><td>2776</td><td>2516</td><td>2303</td><td>1686</td><td>1431</td><td>227</td></tr><tr><td>Multi-pathology image number</td><td>2259</td><td>1683</td><td>1624</td><td>1675</td><td>959</td><td>1109</td><td>117</td></tr><tr><td>Single pathology image number</td><td>1126</td><td>1093</td><td>892</td><td>628</td><td>727</td><td>322</td><td>110</td></tr></tbody></table>

<figure><img src="image_4.png" /><figcaption>**Fig. 1.** Overview of the ChestX-ray14 Dataset.</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>**Fig. 2.** Overall flowchart of the methodology used for the multi-label classification of diseases from Chest X-ray images.</figcaption></figure>

### 3.3.1. U-Net for lung segmentation and mask generation

U-Net is a convolutional neural network architecture specially designed for medical image segmentation, developed by Ronneberger

et al. [34]. As its name suggests, the network has a symmetric U-shaped architecture and is divided into two main parts: the encoder and the decoder. The encoder, composed of convolutional layers followed
<figure><img src="image_6.png" /><figcaption>Fig. 3. Architecture of the U-Net segmentation network.</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>Fig. 4. A sample of chest x-ray images and their corresponding masks: (a) and (c) original CXRs, (b) and (d) masks obtained using U-Net. The red square in the images highlights the area that was not targeted by U-Net, despite being affected by the disease.</figcaption></figure>

by ReLU and max pooling, works to reduce the spatial dimension of the image while extracting complex features. This part acts as a feature extractor, capturing the essential elements of the image. On the other hand, the decoder is dedicated to reconstructing the segmented image from the features identified by the encoder. It uses transposed convolutional layers (or Upconvolution) and concatenation layers to progressively restore the original spatial resolution of the image, thus functioning as a segmented image generator. What makes the U-Net architecture particularly effective are the “skip” connections between the encoder and the decoder. These connections allow for the transfer of contextual information and low-level features directly across the network, enabling precise segmentation while retaining important details of the image. The output images produced by the decoder have binary pixel values of 0 or 1, where 0 corresponds to black and 1 to white. This results in the creation of black and white masks that serve to isolate and highlight areas relevant for further analysis, thereby facilitating the interpretation and evaluation of pulmonary features. The structure of this segmentation architecture is illustrated in Fig. 3.

To train, validate and test the U-Net model, we used the open-source Kaggle dataset “Chest Xray Masks and Labels” [35]. This dataset contained 704 chest x-ray images and their corresponding masks. Once the lung segmenter was trained, we used it to generate masks of the lung images in our dataset. Fig. 4 illustrates the masks generated with U-Net.

As shown in Fig. 4(a) and (b), for CXR of lungs without pathologies, we obtained an accurate mask of the lung contours. However, challenges arise when pathologies are present, as shown in Fig. 3(c) and (d), where U-Net masks fail to capture crucial pathological regions. For instance, in these images showing effusion disease at the bottom of the left lung, marked by a red square, the mask completely omits

the affected area. Instead, the mask only includes healthy lung tissue, while the pathological region, which appears in white on the X-ray, is not represented.

This omission represents a significant drawback, as it results in critical diseased areas being excluded from the mask. Consequently, these regions are not included in subsequent analysis and evaluations of lung features, which can adversely affect diagnostic accuracy and effectiveness. Failure to account for all relevant pathological features can affect the model's overall performance and its ability to provide accurate diagnoses.

This limitation in the performance of U-Net is likely due to its training in images that do not include diseases, which restricts its ability to accurately segment areas affected by disease. This issue is critical because it means that relevant pathological areas are not taken into account in the subsequent analysis and evaluation of lung features.

To address this issue, we developed a novel approach for generating masks that include areas of pathologies. This approach will be outlined and discussed in the following section.

### 3.3.2. Advanced pathology mask generation

To create precise pathology masks on radiographic images, we devised a novel segmentation approach. As illustrated in Fig. 5, our strategy begins with applying the CycleGAN model on radiographic images to generate a healthy restoration of the lungs. Following this, an image subtraction is performed between the 'pseudo-healthy' restored image and the original pathological image. This operation produces a pathology map that accurately delineates the areas affected by anomalies within the original image. This map is then converted into a binary format, resulting in a highly precise mask of the identified pathologies.
<figure><img src="image_8.png" /><figcaption>Fig. 5. The proposed methodology for pathology mask generation in Chest X-ray images.</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>Fig. 6. (a) CycleGAN model contains two mapping functions $G: A \to B$ and $F: B \to A$, and associated adversarial discriminators $D_B$ and $D_A$. $D_B$ encourages $G$ to translate $A$ into outputs indistinguishable from domain $B$, and vice versa for $F$. (b) Forward cycle-consistency loss: for each image from the domain $A$: $a \to G(a) = \hat{b} \to F(G(a)) = \hat{a} \approx a$. (c) Backward cycle-consistency loss: $b \to F(b) = \bar{a} \to G(F(b)) = \bar{b} \approx b$.</figcaption></figure>

## Generation of pseudo-healthy images with CycleGAN

Developed by Zhu et al. in 2017 [36], the Cycle-Consistent Adversarial Network, commonly referred to as “CycleGAN”, stands as a specific variant of Generative Adversarial Networks (GANs). It is specifically designed for unpaired image-to-image translation tasks where paired training data are unavailable.

As depicted in Fig. 6, CycleGAN incorporates two generators and two discriminators. The generator, or generative model, is designed to produce synthetic images. In contrast, the discriminator is tasked with judging the realism of these generated images. As a result, the training process aims to enhance the generators' capacity to create images so lifelike that the discriminators categorize them as real samples. Furthermore, CycleGAN integrates the concept of “cycle consistency” into the GAN framework, establishing both forward and backward cycle consistencies. This allows the translation of images from one domain (A) to another domain (B) without the need for paired training samples [37]. It requires that an image, after being processed by both generators, be restored to its original form [38].

Consequently, CycleGAN is governed by two primary loss functions: the cycle consistency loss and the adversarial loss. The adversarial loss, which is implemented across both directional mappings, employs the least squares loss function to evaluate the difference between the expected and the predicted outputs. CycleGAN further introduces the concept of identity loss. This concept is based on the assumption that if the mapping function $G: A \to B$ is designed to translate images from domain A to domain B, then, logically, when fed an image already belonging to domain B, the function should yield an output that closely resembles the input image ($a \to F(a) \approx a$, and $b \to G(b) \approx b$). Hence, the expectation is that there should be minimal to no difference between the input and output images in such cases, reinforcing the model's ability to maintain the identity of images

that do not require translation between domains. Consequently, the objective for the whole model optimization is to solve Eq. (1):

$$G^*, F^* = \arg\min_{G,F} \max_{D_A,D_B} \mathcal{L}(G, F, D_A, D_B) \quad (1)$$

The architecture for the networks involves the use of an encoder-decoder configuration for the generators, and a standalone encoder configuration for the discriminators. The generators incorporate a ResNet architecture with nine residual blocks, thus denoted as ResNet-9. Meanwhile, the discriminator utilizes a 70 × 70 PatchGAN architecture, which aims to classify whether 70 × 70 overlapping image patches are real or fake. The model is trained with a mini-batch size of 1, using the Adam algorithm with a learning rate of 0.0002.

Fig. 7 illustrates the CycleGAN workflow for converting images with pathologies into images without pathologies i.e. to a healthy restoration of the lungs. A X-ray image presenting pathologies, sourced from the set A which is a collection of numerous images with pathologies, is processed by the generator $G_{AB}$ to produce an image without pathologies. The discriminator B evaluates this generated image to determine its authenticity, drawing upon its observations of a multitude of images without pathologies from the set B. It then outputs a binary decision, 0 or 1, based on its determination of whether the image resembles an image without pathologies or not. Subsequently, this generated image, ostensibly belonging to class B, serves as the input for the generator $G_{BA}$, which aims to invert the process by reintroducing pathologies into the image, thus restoring its original pathological state. Thus, $G_{BA}$ executes the reverse process, producing a synthetically generated image that should be classified as belonging to class A. This approach ensures that the final generated image, known as cycle A, should ideally match the original real input image from domain A. The quality of this reconstruction is evaluated
<figure><img src="image_10.png" /><figcaption>Fig. 7. CycleGAN architecture adapted in our study for the scenario of images with pathologies vs. images without pathologies.</figcaption></figure>

<figure><img src="image_11.png" /><figcaption>Fig. 8. Images obtained by the CycleGAN model.</figcaption></figure>

through the cycle consistency loss metric, which measures the similarity between the original image from domain A and its reconstructed version after it has been transformed to domain B and then reverted back to domain A. A lower discrepancy between these images indicates a high-quality of reconstruction, highlighting the effectiveness of the CycleGAN in maintaining image integrity across the translation cycle.

The CycleGAN model is applied to images exhibiting pathologies, aiming to produce images that depict the lungs in a healthy condition. Fig. 8 presents examples of the image translation achieved by the CycleGAN model, where the generated images show that the lungs are healthy and without pathologies. Moreover, the reconstructed images demonstrate the model's capability to revert the lungs back to their original state, showcasing pathologies. This illustrates the model's effectiveness not only in generating images that present a normal, healthy appearance of the lungs but also in its ability to accurately restore the original pathological conditions, highlighting the efficiency and precision of the CycleGAN model in medical image processing.

## Creation of pathology masks through image subtraction and binarization

Following the generation of images depicting a healthy restoration of the lungs, the subsequent step involves conducting an

image subtraction between the restored version, deemed pseudo-healthy, and the original image displaying pathologies. This operation yields a pathology map that accurately delineates the areas affected by pathologies in the original image. To convert this map into a binary format, we employ the Otsu method, an automatic thresholding technique designed to find the optimal threshold for binarizing a grayscale image [39]. Otsu method identifies the optimal threshold that maximizes the variance between classes, which is equivalent to minimizing the variance within each class. This technique effectively separates the image into distinct regions based on their intensity levels [40].

This process leads to the development of an extremely accurate mask, specifically targeting the regions affected by pathologies in the image. As illustrated in Fig. 9, our novel approach excels at precisely delineating critical regions of interest, ensuring that all pathological areas are included in the masks.

Our approach is distinguished by its ability to generate accurate representations of pathological regions, overcoming the limitations of conventional segmentation methods that may fail to accurately identify critical disease areas. This improvement in mask generation leads to enhanced performance in subsequent tasks, underscoring the effectiveness of our segmentation approach in advancing the field of medical image analysis.

By delivering precise visualizations of pathological areas, our approach significantly enhances diagnostic capabilities, providing a
<figure><img src="image_12.png" /><figcaption>**Fig. 9.** Examples of masks generation using our proposed segmentation approach. Column (a) shows the original image with pathology, Column (b) displays the healthy lung image generated using the CycleGAN model, Column (c) illustrates the pathology mapping obtained by subtracting the original and the generated images, and Column (d) presents the mask obtained after binarization.</figcaption></figure>

deeper understanding of the underlying conditions. The generated masks facilitate more accurate and thorough assessments, thus improving disease evaluation and management.

It is important to highlight that due to the lack of ground truth masks in the ChestX-ray14 dataset, we were unable to calculate the Dice score, which is a metric usually used to evaluate segmentation tasks and relies on reference masks. Instead, we evaluated the masks generated using our proposed CycleGAN-based approach through both visual inspection and quantitative results provided by classification metrics such as Accuracy, Precision, Recall, F1-score, and Area Under the Curve (AUC). These metrics allowed us to evaluate the effectiveness of the masks generated by our approach in classifying lung diseases and validate the performance of our proposed approach.

So in the rest of our study, we used the masks produced by our CycleGAN-based approach to extract features from the images.

## 3.4. Features extraction

Using the generated masks that accurately define the regions of interest (ROIs) affected by pathologies of the original images, we extracted radiomic features through the PyRadiomics tool [41]. This will ensure that the features are extracted from the areas defined by the masks, thereby focusing the analysis on the pathological aspects within the images. Radiomics, which are recognized and standardized by the IBSI (Image Biomarker Standardization Initiative), enables the capture of subtle patterns and intricate details within image data that are often imperceptible to the human eye. By extracting these features, we can uncover the underlying characteristics that provide valuable insights for differentiating between different diseases. The computed features for each group are shown in Table 2.

After extracting these radiomic features, we noticed that some of them had identical values for all the studied classes of images. Therefore, these features, which did not provide any useful distinction between classes, were eliminated. Thus, we obtained 62 distinctive features that provide discriminating information about the images.

## 3.5. Features selection

In our study, to identify the most relevant features for each class even before classification, we implemented a features selection approach. Indeed, features selection is essential for optimizing the performance of machine learning models. It aims to identify and retain only the most relevant and significant features of the data, eliminating any redundant or non-essential information. This approach allows focusing the analysis on the key features that enable the distinction between different classes, thus improving the accuracy and efficiency of the models. Additionally, our features selection approach is useful in multi-label cases, where it distinguishes the significant features relevant to each class even when multiple diseases are present in the same image, further aiding in precise medical diagnosis and treatment planning, especially in complex cases where precise identification of diseases is essential.

Our approach begins with Hierarchical Cluster Analysis (HCA) to group the extracted features based on their similarities, providing a clear structure for further analysis. These clusters are then subjected to a critical assessment using a statistical test (Test$_{value}$) designed to select features that are statistically significant and highly relevant to our model's classification capability.

### 3.5.1. Clustering using hierarchical cluster analysis (HCA)

To divide our data into groups without presupposing the number of clusters, we used Hierarchical Cluster Analysis (HCA). HCA is a statistical method used to identify clusters within the data by analyzing the similarities among samples [42]. In this study, clustering is achieved using Ward's linkage criterion, which aims to minimize the growth of total intra-cluster variance throughout the clustering procedure. The outcome of this analysis is usually illustrated in a dendrogram, where shorter distances between branches indicate greater similarities among the samples. By applying HCA to the extracted features, we group them based on their similarities, thus organizing the data into clusters. Then, the features in each cluster are evaluated using a Test$_{value}$.
<table><caption>**Table 2**
Summary of the extracted features across each radiomics feature group.</caption><thead><tr><th>Feature group</th><th>Features</th></tr></thead><tbody><tr><td>First-order statistics</td><td>Mean, median, variance, standard deviation, minimum, maximum, 10th percentile, 90th percentile, energy, total energy, entropy, range, interquartile range, skewness, kurtosis, uniformity, mean absolute deviation, robust mean absolute deviation, root mean squared</td></tr><tr><td>Gray Level Co-occurrence Matrix (GLCM)</td><td>Contrast, dissimilarity, homogeneity, energy, angular second moment, correlation</td></tr><tr><td>Gray Level Size Zone Matrix (GLSZM)</td><td>Small area emphasis, large area emphasis, gray level non-uniformity, gray level non-uniformity normalized, size-zone non-uniformity, size-zone non-uniformity normalized, zone percentage, gray level variance, zone variance, zone entropy, low gray level zone emphasis, high gray level zone emphasis, small area low gray level emphasis, small area high gray level emphasis, large area low gray level emphasis, large area high gray level emphasis</td></tr><tr><td>Gray Level Run Length Matrix (GLRLM)</td><td>Short run emphasis, long run emphasis, gray level non-uniformity, gray level non-uniformity normalized, run length non-uniformity, run length non-uniformity normalized, run percentage, gray level variance, run variance, run entropy, low gray level run emphasis, high gray level run emphasis, short run low gray level emphasis, short run high gray level emphasis, long run low gray level emphasis, long run high gray level emphasis</td></tr><tr><td>Neighboring Gray Tone Difference Matrix (NGTDM)</td><td>Coarseness, contrast, busyness, complexity, strength</td></tr><tr><td>Gray Level Dependence Matrix (GLDM)</td><td>Small dependence emphasis, large dependence emphasis, gray level non-uniformity, gray level non-uniformity normalized, dependence non-uniformity, dependence non-uniformity normalized, gray level variance, dependence variance, dependence entropy, dependence percentage, low gray level emphasis, high gray level emphasis, small dependence low gray level emphasis, small dependence high gray level emphasis, large dependence low gray level emphasis, large dependence high gray level emphasis</td></tr></tbody></table>

### 3.5.2. Selection of statistically significant features using a Test<sub>value</sub>

For each identified cluster, we calculated the Test<sub>value</sub> as well as the p<sub>value</sub> to identify the most statistically significant features. The defined test is a statistical test designed to compare, for each feature in a cluster, the average of this feature to the overall mean of the feature in the data specific to a disease class. The test used for a feature in a cluster c is defined by:

$$Test_{value} = \frac{\bar{x}_c - \bar{x}}{\sqrt{\frac{s^2}{n_c} \left( \frac{n-n_c}{n-1} \right)}} \quad (2)$$

Where $\bar{x}_c$ represents the mean of a feature in the specific cluster c, $\bar{x}$ is the overall mean of the feature in the disease-specific data, s² is the overall variance of the feature in the disease data, n_c is the number of observations in the cluster c, and n is the total number of the disease observations.

The p<sub>value</sub> is calculated to assess the significance of the Test<sub>value</sub>. A low p<sub>value</sub> indicates that the test is statistically significant, and therefore that the feature characterizes the cluster. This means that this feature is particularly informative for this specific cluster. All the p<sub>value</sub>s obtained were significant (less than 0.05). Our attention is directed towards features exhibiting a positive Test<sub>value</sub>, which indicates that the mean of the cluster exceeds the overall mean. This criterion marks the importance of such features, indicating their essential role in distinguishing between different classes within the dataset.

By combining the features identified as important for each cluster of the same disease, we obtain a set of distinctive features for that disease. Thus, these selected features allow for an effective differentiation between the various disease classes in our study.

To identify the most significant features, we applied the following approach: First, we ranked the Test<sub>value</sub> of the features in descending order. This ranking allows us to prioritize the features based on their statistical importance. Next, we calculated the differences between consecutive Test<sub>value</sub>, i.e., $e_i = V_i - V_{i+1}$. This step helps identify significant jumps between the Test<sub>value</sub>. Finally, we determined the index K where the difference $e_i$ is maximum, indicating points where the significance of the features changes notably, i.e., K = index of max($e_i$). By selecting the first K features, we ensure to retain those with the greatest significance according to the Test<sub>value</sub>. Therefore, this selection

process ensures that the selected features are highly relevant and contribute significantly to the differentiation of these specific pathological conditions within the dataset.

For the multi-label classification of the two diseases, effusion and infiltration, we defined three sets of features: The first set includes features from images containing only effusion. The second set comprises features from images presenting only infiltration. The third set consists of features from images containing both effusion and infiltration.

We then considered two scenarios for features selection:

* Scenario 1: We included all the features identified as most significant for the three sets to perform the multi-label classification of diseases.

* Scenario 2: We selected the most significant features for the three sets excluding the common features among them for the multi-label classification of diseases.

By applying this features selection approach, we identified 47 features as the most significant in the first scenario, and 19 features in the second scenario, for the multi-label classification of the two diseases: effusion and infiltration. Table 3 presents the selected features. Indeed, through our features selection method, we determined that the features in the first row were particularly the most significant only for the class corresponding to effusion (i.e., images showing effusion disease). These features were not among the most relevant for the infiltration disease. The features located in the second row of Table 3 were identified as the most significant for images presenting the infiltration disease. The features “Mean”, “Median” and “Root Mean Squared” were the most significant for images exhibiting both diseases concurrently. The features listed in the fourth row of Table 3 were common for images presenting effusion disease as well as for images presenting infiltration disease. The features “Minimum” and “10Percentile” were identified as the most significant for images showing effusion disease while still retaining their importance for images presenting both diseases. The features mentioned in the sixth row of Table 3 proved to be the most significant for images presenting infiltration disease and remained relevant for images containing both diseases. Finally, the features listed in the last row of Table 3 were deemed the most significant for images exhibiting only effusion, images presenting only infiltration and images containing both diseases concurrently.
<table><caption>**Table 3**
Summary of the selected features for the different condition.</caption><thead><tr><th>Condition</th><th>Selected features</th></tr></thead><tbody><tr><td>Effusion</td><td>Small Area Emphasis, Small Dependence Emphasis, Zone Percentage</td></tr><tr><td>Infiltration</td><td>Kurtosis, asm3, Contrast2, energy4, asm4, Contrast1, asm1, energy3, energy2, Contrast3, asm2, Contrast4, energy1</td></tr><tr><td>Effusion+Infiltration</td><td>Mean, Median, Root Mean Squared</td></tr><tr><td>Effusion<br>Infiltration</td><td>Large Area Emphasis, Zone Variance, Large Dependence Emphasis</td></tr><tr><td>Effusion<br>Effusion+Infiltration</td><td>Minimum, 10Percentile</td></tr><tr><td>Infiltration<br>Effusion+Infiltration</td><td>Total Energy, homogeneity2, dissimilarity3, dissimilarity2, Gray Level Non Uniformity3, homogeneity3, dissimilarity4, dissimilarity1, homogeneity1, homogeneity4</td></tr><tr><td>Effusion<br>Infiltration<br>Effusion+Infiltration</td><td>Dependence Variance, Run Variance, Run Percentage, Run Entropy, Zone Entropy, Size Zone Non Uniformity, Gray Level Non Uniformity2, Short Run Emphasis, Long Run Emphasis, Dependence Entropy, Dependence Non Uniformity, Run Length Non Uniformity, Gray Level Non Uniformity</td></tr></tbody></table>

Therefore, through our features selection approach, we were able to identify the most significant features specific to each disease while also distinguishing those that remain important when both diseases are present simultaneously. This strategy revealed subtle distinctions in the influence of different features, providing a deep understanding of the discriminative markers between the diseases studied. This approach enhances the ability of classification models to accurately distinguish between different pathological conditions, thus contributing to more reliable diagnoses and a better understanding of the diseases and the interactions between different pathological manifestations.

### 3.6. Classification

All the selected features were fed into machine learning algorithms to perform the multi-label classification task targeting effusion and infiltration. The data was partitioned such that 80% was allocated for training, while the remaining 20% served as test data (randomly chosen). For this task, two machine learning algorithms were used: Random Forest (RF) and Extreme Gradient Boosting (XGBoost). These algorithms were chosen because of their strong performance in handling complex classification tasks.

Random Forest (RF) is an ensemble learning model that uses a “parallel assembling” which constructs multiple decision tree classifiers in parallel on various subsamples of the datasets and takes their majority vote for classification [43].

Extreme Gradient Boosting (XGBoost) is an ensemble learning algorithm based on the concept of “boosting” which aggregates the predictions of a set of “weak” learners to construct a “strong” learner through sequential additive training. This approach sequentially trains an ensemble of shallow decision trees, where each successive tree is trained on the error residuals left by the previous model. By employing the gradient, it aims to minimize the loss function similar to how weights are optimized in neural networks using gradient descent [44]. This process improves the model generalization capabilities and performance and reduces overfitting. The final model prediction is a weighted sum of all the tree predictions.

In our study, we employed the grid search technique to identify the optimal hyperparameters for the machine learning models. Grid search is a search method that systematically evaluates all possible combinations within a predefined set of hyperparameter values, with the aim of determining the configuration that yields the best performance. The RF hyperparameters were tested to determine those that performed best with the experiment data; The n_estimators: [50, 100, 200, 300] and criterion: ['gini', 'entropy'] were tested. The best values of the RF hyperparameters that were selected are 200 trees and a gini criterion. The default hyperparameters for the XGBoost algorithm are provided by the implementation of xgboost. The tree-based models (gbtree) which is the type of model to run at each iteration, is the

general parameter selected for the XGBoost model. The model has 100 trees and the maximum depth of a tree is 6.

To assess the performance of our machine learning models, we employed several metrics, including the confusion matrix and its associated metrics such as Accuracy, Precision, Recall, F1-score, and the Area Under the Curve (AUC).

## 4. Results

In this section, we present the results achieved from the techniques investigated in this paper with the different tests performed. To ensure the stability of the results, we performed ten different split partitions of the data into training and testing sets and presented the results in terms of mean and standard deviation.

### 4.1. Ablation study

In Table 4, we present a comparison between the results obtained using U-Net for mask generation and those achieved by our novel pathology mask generation technique for the multi-label classification of effusion and infiltration. This comparison highlights the differences in performance and illustrates the advantages of our approach in enhancing the precision of disease classification.

In Table 5, we provide a comparative analysis of the results achieved with our proposed mask generation method for the multi-label classification of effusion and infiltration, examining the impact of implementing features selection approach, scenarios 1 and 2, versus not employing it. This comparison aims to highlight the effectiveness of features selection in improving the accuracy and AUC of diseases classification using our methodology.

In Table 6, we present the detailed results of the best performing model identified, which is the XGBoost model using our proposed mask generation method with scenario 2 for features selection.

In Fig. 10, we present the Receiver Operating Characteristic (ROC) curves for the multi-label classification of all diseases within the ChestX-ray14 dataset, based on the testing subset and employing our best-performing model with scenario 1 for features selection.

### 4.2. Comparison with existing methods

In Table 7, we present a comparative analysis between our best-performing model and previous studies using the ChestX-ray14 dataset, demonstrating that our model outperforms existing models in the literature. This comparison highlights the improvements and effectiveness that our approach contributes to the field of medical image classification.
<table><caption>**Table 4**

Impact of the mask generation method on the multi-label classification results of effusion and infiltration. Significant values are in bold.</caption><thead><tr><th rowspan="2">Mask generation approach</th><th colspan="2">Accuracy (%)</th><th colspan="2">AUC (%)</th></tr><tr><th>RF</th><th>XGBoost</th><th>RF</th><th>XGBoost</th></tr></thead><tbody><tr><td>With U-Net</td><td>79.15 ± 2.5</td><td>79.56 ± 2.3</td><td>74.47 ± 2.4</td><td>75.63 ± 2.5</td></tr><tr><td>With our proposed method using CycleGAN, image subtraction and binarization</td><td><b>88.26 ± 1.8</b></td><td><b>89.52 ± 1.7</b></td><td><b>85.54 ± 1.9</b></td><td><b>86.55 ± 1.8</b></td></tr></tbody></table>

<table><caption>**Table 5**

Comparison of multi-label classification results of effusion and infiltration using our proposed mask generation approach with and without features selection. Significant values are in bold.</caption><thead><tr><th rowspan="2">Our proposed mask generation approach</th><th colspan="2">Accuracy (%)</th><th colspan="2">AUC (%)</th></tr><tr><th>RF</th><th>XGBoost</th><th>RF</th><th>XGBoost</th></tr></thead><tbody><tr><td>Without features selection</td><td>88.26 ± 1.8</td><td>89.52 ± 1.7</td><td>85.54 ± 1.9</td><td>86.55 ± 1.8</td></tr><tr><td>With features selection (Scenario 1)</td><td>89.32 ± 1.6</td><td>90.59 ± 1.5</td><td>87.21 ± 1.7</td><td>87.83 ± 1.5</td></tr><tr><td>With features selection (Scenario 2)</td><td><b>91.16 ± 1.4</b></td><td><b>92.05 ± 1.3</b></td><td><b>88.92 ± 1.5</b></td><td><b>89.48 ± 1.4</b></td></tr></tbody></table>

<table><caption>**Table 6**

Detailed results of the XGBoost model using our proposed mask generation method with scenario 1 for features selection, for the multi-label classification of effusion and infiltration.</caption><thead><tr><th>Label</th><th>Precision (%)</th><th>Recall (%)</th><th>F1-score (%)</th><th>Accuracy (%)</th><th>AUC (%)</th></tr></thead><tbody><tr><td>Effusion</td><td>90.68 ± 1.4</td><td>96.38 ± 1.2</td><td>93.45 ± 1.3</td><td>91.06 ± 1.4</td><td>88.53 ± 1.4</td></tr><tr><td>Infiltration</td><td>92.95 ± 1.3</td><td>97.29 ± 1.1</td><td>95.07 ± 1.2</td><td>93.04 ± 1.2</td><td>90.42 ± 1.3</td></tr></tbody></table>

<figure><img src="image_13.png" /><figcaption>**Fig. 10.** ROC curves and AUC scores of all diseases on the test set of the ChestX-ray14 dataset using our proposed model.</figcaption></figure>

# 5. Discussion

## 5.1. Ablation study

After introducing into the system images presenting either effusion or infiltration pathology, or both diseases simultaneously, we evaluated the impact of mask generation methods on the classification performance of lung diseases. Our assessment focused on how our

segmentation method influenced classification outcomes using classification metrics rather than traditional segmentation metrics like the Dice score due to the lack of ground truth masks. We tested two main approaches: the first used a U-Net model, while the second applied our CycleGAN-based technique to generate new masks. These masks were then used in the classification process to analyze their effectiveness. From Table 4, we notice that both the RF and XGBoost models performed better in terms of accuracy and AUC scores using
<table><caption>**Table 7**
Comparison of AUC results between our best model and existing works using the ChestX-ray14 dataset. Significant values are in bold.</caption><thead><tr><th>Abnormality</th><th>Ma et al. (2019) [28]</th><th>Guan et al. (2020) [27]</th><th>Gündel et al. (2021) [45]</th><th>Chen et al. (2022) [29]</th><th>Yang et al. (2022) [31]</th><th>Jiang et al. (2024) [30]</th><th>Our approach</th></tr></thead><tbody><tr><td>Atelectasis</td><td>77.7%</td><td>78.1%</td><td>78.5%</td><td>75.9%</td><td>78.8%</td><td>79.1%</td><td><b>82.46%</b> ± <b>1.5%</b></td></tr><tr><td>Cardiomegaly</td><td>89.4%</td><td>88%</td><td>89.2%</td><td><b>89.83%</b></td><td>87.5%</td><td>88.5%</td><td>85.13% ± 2.2%</td></tr><tr><td>Consolidation</td><td>75%</td><td>75.4%</td><td>74.7%</td><td>75.12%</td><td>75.6%</td><td>75.3%</td><td><b>83.85%</b> ± <b>1.7%</b></td></tr><tr><td>Edema</td><td>84.6%</td><td>85%</td><td>83.7%</td><td><b>86.36%</b></td><td>85.4%</td><td>85.9%</td><td>84.41% ± 2.4%</td></tr><tr><td>Effusion</td><td>82.9%</td><td>82.9%</td><td>83.6%</td><td>82.66%</td><td>83.7%</td><td>84.2%</td><td><b>85.62%</b> ± <b>1.4%</b></td></tr><tr><td>Emphysema</td><td>90.8%</td><td>90.8%</td><td>92.5%</td><td>88.98%</td><td>93.4%</td><td><b>94.4%</b></td><td>84.44% ± 2.3%</td></tr><tr><td>Fibrosis</td><td>82.7%</td><td>83%</td><td>83.8%</td><td>83.59%</td><td><b>84.9%</b></td><td><b>84.9%</b></td><td>82.07% ± 2.1%</td></tr><tr><td>Hernia</td><td>93.4%</td><td>91.7%</td><td>90.5%</td><td>94.04%</td><td><b>94.4%</b></td><td>92.4%</td><td>76.93% ± 2.9%</td></tr><tr><td>Infiltration</td><td>69.6%</td><td>70.2%</td><td>71%</td><td>70.99%</td><td>72.2%</td><td>71.5%</td><td><b>86.11%</b> ± <b>1.3%</b></td></tr><tr><td>Mass</td><td>83.8%</td><td>83.4%</td><td>82.6%</td><td>83.2%</td><td><b>84.5%</b></td><td>83.7%</td><td>82.86% ± 1.6%</td></tr><tr><td>Nodule</td><td>77.1%</td><td>77.3%</td><td>75.5%</td><td>75.33%</td><td>79.6%</td><td>80.3%</td><td><b>82.53%</b> ± <b>1.5%</b></td></tr><tr><td>Pleural_Thickening</td><td>77.9%</td><td>77.8%</td><td>78.5%</td><td>78.94%</td><td>78.9%</td><td>80.3%</td><td><b>85.35%</b> ± <b>1.7%</b></td></tr><tr><td>Pneumonia</td><td>72.2%</td><td>72.9%</td><td>73.5%</td><td>73.56%</td><td>73.4%</td><td>74.5%</td><td><b>77.81%</b> ± <b>1.4%</b></td></tr><tr><td>Pneumothorax</td><td>86.2%</td><td>85.7%</td><td>84.7%</td><td>87.6%</td><td>86.2%</td><td><b>88.5%</b></td><td>84.16% ± 1.8%</td></tr><tr><td>Average AUC</td><td>81.7%</td><td>81.6%</td><td>81.59%</td><td>81.86%</td><td>82.7%</td><td>83.1%</td><td><b>83.12%</b> ± <b>1.8%</b></td></tr></tbody></table>

our proposed method for generating pathological masks. Using U-Net for mask generation, the RF and XGBoost models obtained, over 10 splits, an average accuracy of 79.15% and 79.56%, and an average AUC score of 74.47% and 75.63%, respectively. Using our method for generating pathological masks, the RF and XGBoost models achieved, over ten splits, an average accuracy of 88.26% and 89.52%, as well as an average AUC score of 85.54% and 86.55%, respectively.

The use of masks obtained via U-Net proved to be less effective, mainly because these masks are restricted to the healthy parts of the lungs and omit the pathological areas crucial for adequate disease characterization. This issue is clearly illustrated in Fig. 4, where the U-Net masks fail to include the areas affected by the disease. This result is likely because U-Net is trained on data without diseases, which limits its capability to detect and segment areas affected by diseases.

In contrast, our approach stands out by ensuring that the masks encompass regions of interest affected by pathologies, enabling the extraction of features from these critical areas. Unlike U-Net, which often misses crucial diseased areas, our approach precisely targets and includes these regions, as clearly demonstrated in Fig. 9. By generating masks that include pathological zones, our approach allows the extraction of features directly from the critical areas of disease. This leads to a more precise and effective classification of pathological conditions. This highlights the significant advantage of our segmentation approach over conventional segmentation methods such as U-Net. Indeed, the major advantage of our approach lies in its ability to accurately target and include specific regions of interest essential for disease characterization. This targeted approach ensures that our model is trained on the most relevant features, significantly enhancing its performance compared to traditional techniques that may inaccurately delineate these critical disease areas.

In addition, our approach stands out with several key advantages in lung disease segmentation, particularly in comparison to traditional methods. First, training a U-Net model to segment a specific disease requires annotated masks for that disease, which can be a major challenge. Indeed, in many cases, annotated masks for each thoracic pathology are not available, and the manual generation of these annotations requires in-depth medical expertise, as well as significant time and resource investment. In contrast, our CycleGAN-based approach overcomes this limitation by not requiring specific masks for each disease during training. This reduces the dependency on manual annotations and allows us to work with non-annotated data, making our method more accessible and adaptable. Furthermore, unlike U-Net, which must be trained separately for each disease using specific masks, our approach is highly generalizable. With CycleGAN, we can easily apply it to new diseases without needing to retrain a model for each case. This ability to adapt to a wide range of pathologies without prior

knowledge of the affected areas allows our method to automatically focus on the relevant pathological regions without requiring human intervention or detailed annotations. This not only improves the efficiency of segmentation, but also enhances its flexibility for a variety of clinical applications.

Although we have assessed the impact of our segmentation approach on classification performance, its broader applicability underscores its potential to advance medical image analysis. Accurate region delineation enhances not only classification, but also significantly benefits various diagnostic processes, providing more precise and comprehensive insights into pathological conditions. So in the rest of the study, our proposed CycleGAN-based segmentation approach is used as the mask generation method.

Using the generated masks that accurately define the ROIs affected by pathologies and the original images transformed to grayscale, radiomic features including First-order statistics, GLCM, GLSZM, GLRLM, NGTDM and GLDM were used for features extraction. To select the most significant features for each pathology, as well as in scenarios where both diseases are present simultaneously, we implemented our proposed features selection methodology that begins with HCA, followed by a rigorous evaluation using a Test<sub>value</sub>. To assess the effectiveness of features selection in the classification process, we analyzed the performance of models with and without the use of our proposed features selection approach with scenarios 1 and 2. According to the results presented in Table 5, it is clear that applying scenario 2 for features selection by excluding the common features among classes leads to significant improvements in model outcomes, highlighting the importance of this step in enhancing classification performance. Without features selection, although the RF and XGBoost models showed good performances, the implementation of our features selection method significantly increased both the accuracy and the AUC score of these models. For instance, without features selection, the RF and XGBoost models achieved average accuracy of 88.26% and 89.52%, and AUC scores of 85.54% and 86.55% respectively. With the implementation of scenario 1 for features selection, the models' accuracy improved to reach 89.32% for RF and 90.59% for XGBoost, while the AUC scores increased to 87.21% for RF and 87.83% for XGBoost. Additionally, with the use of scenario 2 for features selection, the models RF and XGBoost achieved an accuracy of 91.16% and 92.05%, and AUC of 88.92% and 89.48%, respectively. These increases are not only statistically significant, as shown by the bold values, but also clinically relevant since even slight improvements in accuracy and AUC score can have a considerable impact on the reliability of pulmonary disease classification. These results clearly demonstrate that features selection using scenario 2, combined with our proposed mask generation method, significantly improves model performance in lung disease classification,
with an enhancement of 2.93%. This underscores the critical impor-
tance of carefully selecting features for classification, highlighting the
substantial advantage of our approach in refining classification results
by focusing on disease-specific features.

Overall, the results of this study demonstrate the effectiveness of machine learning models, particularly the XGBoost model followed by the RF model, in the task of classifying pulmonary pathologies. Although both models offer high performance, there is room for improvement. The XGBoost model proved to be the most efficient among the two, attributable to its advanced boosting method. Unlike Random Forest, which creates a large number of independent decision trees and relies on the majority vote of these trees for the final classification, XG-Boost adopts a refined and iterative approach. XGBoost model is based on the concept of boosting, aiming to minimize the loss function by sequentially incorporating weak learners, enhanced through gradient descent. This strategy allows XGBoost to continuously adjust its predictions by paying particular attention to instances that were misclassified in previous iterations, thus enhancing its ability to accurately identify pulmonary pathologies in chest X-ray images. Therefore, the XGBoost model is highly recommended for classifying lung diseases from Chest X-ray images. Its use represents a significant advancement in the field of computer-assisted medical diagnosis, offering healthcare professionals a robust tool for accurate classification of lung diseases.

The results presented in Table 6 show that the XGBoost model achieved high performance in the classification of effusion and infiltration diseases. This ability to reliably identify diseases is crucial in the medical field as it plays a determining role in the development of treatment protocols, directly influencing the prognosis and contributing to improve patients' quality of life. These results highlight the importance of our integrated approach, which combines an advanced pathology mask generation method with a rigorous selection of features, to refine classification performance and support clinical decisions.

We assessed the effectiveness of our features selection approach by applying the SHAP (SHapley Additive exPlanations) method to the XGBoost model, which was trained with the complete set of features. The SHAP method represents a significant advancement in interpreting machine learning model outputs. It quantifies the contribution of each features to the final prediction, allowing for an evaluation of each feature's influence on the model. The importance of SHAP features is calculated as the average of the absolute Shapley values. The idea of SHAP features importance is that important features are those with great absolute Shapley values [44]. Fig. 11(a) and (b) illustrates the features identified by SHAP as being the most influential in classifying effusion and infiltration diseases, respectively. These features are ranked in descending order of importance according to their impact on the prediction. These visualizations provide an in-depth understanding of the features guiding the XGBoost model's classification decisions for each pathology in a multi-label classification approach. This SHAP analysis not only reveals the most significant features but also validates the effectiveness of our features selection approach by demonstrating a concordance between the features selected a priori by our approach and those identified as having the greatest impact on the model's predictions by SHAP. This dual technique underscores the effectiveness of our feature engineering strategy in improving the interpretability of the model. It provides valuable insights into the contribution of each feature in diseases classification, offering a deeper understanding of the model's decision-making process and its potential integration into clinical workflows. Thus, our approach confirms its ability to select the most relevant features for the classification of lung diseases, enhancing the accuracy and reliability of pulmonary disease classification through the XGBoost model.

The main advantage of ML models over DL models lies in their
greater interpretability, due to their reliance on feature engineering. ML
models use features with interpretable mathematical meanings, which
enhances the understanding of the model's decision-making process.
In contrast, DL models often function as "black boxes" with complex

structures that make them challenging to interpret [46]. Additionally, ML algorithms are typically less complex and can run on standard computing systems, whereas DL algorithms require significantly more computational power and advanced hardware to handle the extensive data processing and complex mathematical operations involved.

Indeed, the approach developed in this study, through its ability to identify the most significant features for the classification of lung pathologies, makes a notable contribution to medical practice. This approach enables healthcare professionals to understand which aspects of radiographic images are most relevant for distinguishing between different lung conditions, facilitating more accurate and targeted interpretation of radiographic examinations. By leveraging radiomic features, physicians can gain a more detailed understanding of the biological and clinical relevance of diseases. As a result, physicians can make more accurate diagnostic and therapeutic decisions, improving treatment efficacy and potentially patient outcomes. This approach significantly improves the accuracy of medical diagnosis, underlining the importance of carefully selecting features for in-depth analysis of chest diseases, and highlights the potential for improving healthcare quality through advanced medical image processing techniques.

Thereafter, we generalized our approach using the XGBoost model for multi-label classification of the 14 diseases present in the ChestX-ray14 dataset. Our mask generation method was applied to include pathological areas in the masks for a more precise and relevant features extraction. An essential aspect of our approach was the rigorous selection of the most significant features for each of the 14 diseases. In the case of images showing combinations of pathologies, i.e. presenting two or more diseases simultaneously, we limited ourselves only to combinations represented by more than 100 images. This restriction ensured we had a sufficiently large sample size to guarantee the reliability and robustness of our analysis. This approach enabled us to select 58 features deemed most significant for the analysis by considering scenario 1 for features selection, and 15 features with scenario 2 that excludes common features between classes. However, the use of the scenario 2 did not effectively differentiate between the various diseases. Indeed, there were no specific features unique to various classes of diseases, as they shared many common features. Therefore, for the classification of the 14 diseases, we used our features selection approach according to the scenario 1. This targeted selection of features is crucial for accurate and reliable classification of chest diseases, enabling us to optimize the performance of our classification model.

Fig. 10 illustrates the ROC curves of our proposed approach applied to the ChestX-ray14 dataset, providing a quantitative evaluation of the model performance. These curves highlight the model's ability to precisely discriminate between the presence and absence of the 14 diseases, an essential capability for ensuring accurate diagnoses and effectively guiding treatment choices for chest diseases.

5.2. *Comparison with existing methods*

The classification results obtained by our method for the 14 diseases are compared to those of the most recent approaches in Table 7. Our approach achieved an average AUC of 83.12%, surpassing existing methods. Our approach reached the highest AUC scores for 7 out of the 14 diseases (indicated in bold), while the scores for the other 7 diseases remain competitive with the best current results. More specifically, our method clearly outperforms previous studies with significant improvements, such as for atelectasis (3.36%), consolidation (8.25%), effusion (1.42%), infiltration (13.91%), nodule (2.23%), pleural thickening (5.05%), and pneumonia (3.31%). For other diseases, our results align with those reported in the literature. This is largely because previous studies have utilized deep learning (DL) techniques on the ChestX-ray14 dataset, incorporating custom loss functions to effectively handle class imbalance. In contrast, our study used machine learning (ML) methods without addressing class imbalance issue. However, the results demonstrate that with careful feature engineering and accurate
<figure><img src="image_14.png" /><figcaption>Fig. 11. Results from the SHAP model highlighting the most influential features for the multi-label classification of the two diseases: (a) effusion and (b) infiltration.</figcaption></figure>

segmentation, ML models can achieve performance competitive with DL approaches. Furthermore, we achieved an accurate classification of the no finding images (i.e., images without any disease).

These improvements underscore the effectiveness of our segmentation approach, which generates precise masks focusing on pathological regions, in refining the classification of chest diseases, thereby offering promising prospects for more effective treatment strategies and better patient outcomes.

Therefore, our study not only fills a gap in the exploration of radiomic features for the classification of chest diseases, but also offers a better understanding and a more precise interpretation of chest diseases from chest X-ray images. This advancement significantly contributes to the improvement of computer-assisted diagnostic techniques, with a potentially significant impact on treatment strategies and the management of patients suffering from chest diseases.

## 5.3. Limitations

Our study presents some limitations. First, due to the absence of ground truth masks in the ChestX-ray14 dataset, we evaluated the generated masks using visual inspection and the quantitative results of classification metrics. Although masks successfully highlight pathological regions, their effectiveness is closely tied to the quality of the generated images. While CycleGAN has demonstrated its ability to produce images with a normal lung appearance, the sharpness of these images is not always optimal. Enhancements to the CycleGAN architecture may be necessary to further improve the quality of the generated images. In our experiments, we used the default hyperparameters outlined in the original CycleGAN paper [36]. Although these parameters delivered promising results, additional hyperparameter optimization could potentially result in higher-resolution images with

improved clarity, thereby refining the generated masks. Another limitation lies in the classification performance. Although the results are encouraging, further refinements could enhance model's effectiveness. The success of classification is closely linked to the quality of the extracted features. While our current feature set delivered promising outcomes, investigating additional feature extraction techniques could lead to even better results. Moreover, implementing methods to address class imbalance could significantly enhance the model's performance. Furthermore, we acknowledge a potential limitation in the generalizability of our findings, despite utilizing the ChestX-ray14 dataset, which comprises a large number of images collected from multiple acquisition centers. Indeed, variability introduced by differences in scanner types and imaging protocols could affect classification performance. Future research could focus on harmonizing data from various acquisition centers or scanners to mitigate this variability and improve model's robustness and adaptability to diverse datasets.

## 6. Conclusion

In this paper, we performed machine learning model focused on radiomics for the multi-label classification of chest diseases from chest X-ray images. We presented two contributions that aim to improve the performance of chest diseases classification. Our first contribution lies in the development of a novel segmentation approach based on the CycleGAN model for pathological masks generation designed to encompass the areas affected by pathologies within the region of interest. This method optimizes the extraction of radiomic features from regions directly linked to pathologies, significantly improving the performance of disease classification. Indeed, our approach improved the AUC of the XGBoost model by 10.92% compared to using the U-Net model for mask generation. The second contribution involved a refined
features selection approach, aimed at identifying the most significant and relevant features for each pathology. This approach improves the efficiency of the classification model by precisely targeting key features specific to each condition. By integrating these two contributions, our pipeline offers promising results, surpassing the performance of previous studies. This study represents a significant advancement in the field of medical image analysis, providing a robust framework for more accurate and efficient disease diagnosis. The proposed approach was implemented in Python and will be provided upon request.

## CRediT authorship contribution statement

**Aya Hage Chehade:** Writing – review & editing, Writing – original draft, Visualization, Validation, Software, Methodology, Formal analysis. **Nassib Abdallah:** Writing – review & editing, Visualization, Validation, Supervision, Methodology. **Jean-Marie Marion:** Writing – review & editing, Visualization, Validation, Supervision, Methodology. **Mathieu Hatt:** Writing – review & editing. **Mohamad Oueidat:** Supervision. **Pierre Chauvet:** Supervision.

## Ethical approval

This study uses public database cited in the references and therefore this section is not applicable.

## Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

## Declaration of competing interest

The authors declare that they have no conflict of interest.

## Declaration of Generative AI and AI-assisted technologies in the writing process

During the preparation of this work the authors used AI-assisted technologies in order to enhance the English language quality. After using this tool, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication.

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