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# A Survey on DL Based Frameworks for COVID-19 Radiological Diagnosis

J. Kishan, G. Kalaichelvi, R. Surendiran, Bhadru Amgothu

► To cite this version:

J. Kishan, G. Kalaichelvi, R. Surendiran, Bhadru Amgothu. A Survey on DL Based Frameworks for COVID-19 Radiological Diagnosis. 6th International Conference on Computer, Communication, and Signal Processing (ICCCSP), Feb 2022, Chennai, India. pp.36-45, 10.1007/978-3-031-11633-9_4. hal-04388142

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# A Survey on DL based Frameworks for COVID-19
Radiological Diagnosis

J. Kishan¹[0000-0003-0768-6212], G. Kalaichelvi¹, R. Surendiran¹[0000-0002-6605-5886],
Bhadru Amgothu¹[0000-0002-7598-7423]*

¹ SAMEER - Centre for ElectroMagnetics, Meity, GoI, Chennai, India
amgothubhadru.sameer@nic.in

**Abstract.** The ongoing Coronavirus disease (COVID-19) pandemic still necessitates emphasis on diagnosis and management of the outbreaks due to the emergence of new variants. This paper is an extensive survey on the implementation of Deep Learning (DL) models used for diagnosing COVID-19 from chest imaging, enriched with quantitative measures and regulatory aspects. The authors have searched, collated and categorised various models and techniques that reported different architectures with respect to COVID-19 diagnosis in the literature. This survey also briefs about quantifying metrics and the reported results are enumerated, also regulatory frameworks for public use of Artificial Intelligence (AI) in medical devices are comprehended.

**Keywords:** AI, DL, CNN, GAN, LSTM, medical devices

## 1 Introduction:

COVID-19 pandemic had devastating effects affecting over 247,968,227 cases (as of 5th Nov '21) and 5,020,204 deaths as per World Health Organisation [1], leading to large ramifications in public health management. Though the search for prevention and treatment have shown fruits, the concern of new outbreaks due to variants intensifies further research.

When bioindicators like Reverse Transcription-Polymerase Chain Reaction (RT-PCR) and other methods such as serologic tests and viral throat swab testing, is primarily used for diagnosing, for confirmed or suspected COVID-19 cases, chest imaging is used to decide on the severity of the infection and further treatment plan [2]. However, limitation on X-rays and Computed Tomography (CT) imaging is the requirement of a human expert for diagnosis. This could cause a bottleneck in the smooth process of COVID treatment management. AI, specifically DL, can relieve this bottleneck.

This paper summarises research of various AI and Deep Learning (DL) implementations published during the period 2019-2021 on chest imaging namely X-ray & CT and lung cough pattern. Regulatory aspects with respect to the marketing and wide scale distribution of AI software as medical devices is also covered by this survey.

### 1.1 Deep Learning on Healthcare use cases

DL with its comprehensive feature extraction and self-learning ability is obviously a proven candidate for diagnosing COVID. As the application of DL technologies offer rapid and accurate results, a lot of research focus has been drawn towards its implementation on studying radiological images.

Multiple studies have shown the effectiveness and accuracy equaling human potential savings of human error and time[3]. Computer Vision (CV) and DL have extensive literature for diagnosis [4], such as various tumor detection [5] [6], diabetic retinopathy, skin laceration [7]. Several studies have reported about the accuracy of ML models
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for diagnosis from imaging versus human experts for comparison, particularly as reported [8], CV19-Net showed an Area Under Curve of 0.94 versus 0.85 achieved by radiologists.

## 2 Organisation of this work

The following figure 1(a) represents the layout of the contents provided in this survey

<figure><img src="image_3.png" /><figcaption>Fig. 1. (a) Organization of the Survey</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>(b) Prediction of models referenced</figcaption></figure>

During the survey it was observed that the prediction accuracy is reported predominantly in order of 90 percentage as diagrammatically presented in the figure 1(b) with their respective reference numbers along the X-axis.

The following section discusses the methodology to quantify with the corresponding metrics. Then sparse primary datasets & secondary datasets in use are discussed. Subsequently the literature on Deep Neural Network (DNN) on COVID-19 diagnosis are sorted based on the architectures and finally the regulatory frameworks are outlined.

## 3 Methodology and Metrics

This survey was done in phases, starting with identifying published journals and articles about the implementation of AI/ML models regarding pneumonia and the covid-19 diagnosis from reputed repositories like 1) IEEE 2) ScienceDirect 3) PubMed and 4) Scopus. Then the identified papers were evaluated based on the criteria like citations, model metrics and summarised with respect to quantitative parameters as described in [7][8]. And finally the publications were sorted and summarised based on models like Convolutional Neural Networks(CNN), Generative Adversarial Neural (GAN) Networks, Transfer Learning, Long short Term Memory (LSTM), Reinforcement learning and Auto Encoders.

The metrics used in the literature to evaluate the performance of models are listed below.

* **Classification Accuracy:** Number of times predictions that the model correctly predicted, obtained by dividing Number of correct predictions to total predictions.

* **Precision:** It's fraction of samples to total samples, "It can be stated as
  True Positives / (True Positives + False Positives)" (1)

* **Recall:** Denotes how sensitive the model is to correct predictions. Recall and TPR are the same

$$ \frac{\text{True Positives}}{\text{True Positives} + \text{False Negatives}} \qquad (2) $$

* **F1 score:** Combination of precision and recall, The F1 score reaches its best value at 1 and worst at 0. The F1 score is represented as:

$$ F1 = 2 * \frac{(precision * recall)}{(precision + recall)} \qquad (3) $$

* **AUC:** This metric stands for "Area under the Receiver Operating Characteristic (ROC) Curve". AUC provides a measure of metrics across all classification thresholds.
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# 4 Datasets

The performance of the model heavily depends on the quality of the dataset it is trained on [9]. Neural networks are non-linear models that work on by figuring out the non-linear relationship between variables and hence they can be said to have low bias and high variation. Thus, we can reliably generalise a need for a good balanced dataset for ensuring that models for real life solutions. However many real-life datasets are smaller and often constrained, especially in Medical field. Among the papers and stud- ies reported, the significant sources of the dataset are as follows.

'Italian Society of Medical and Interventional Radiology (SIRM) COVID-19 Data-
base' has 1525 X-ray images and is being reported in plenty [10]. Open sources datasets
are listed in GitHub like that of Joseph Paul Cohen et al named as Novel CoronaVirus
2019 Dataset and open source Datasets are listed in the Kaggle. The studies have used
both primary datasets i.e. data collected during the research and approved by ethics
board and secondary datasets which are collated from open sources and repositories.

An similar Indian datasets is not found however there are some publications where the chest xrays samples augmented with existing for testing their models are published. The xrays found in the datasets are primarily in jpeg format and resized to smaller size for the purpose. This is potential gap where dataset focused on Indian datasets can be done.

5 Deep Neural Networks Architectures

5.1 Convolutional Neural Networks (CNNs)

A CNN is a DL neural network configured for processing structured arrays of data, such as images. CNNs have a proven record for image classification, which is significant, as some studies have shown that they equal performance to human experts [11]. CNN has been extensively studied for medical diagnosis because of their advantage over linear ML methods as they can effectively extract features and make predictions from complex data and multi-dimensional data like images [12]. Many studies have implemented various types of CNN with basic layers and combinations of popular layers architectures.

Chest X-Rays

Mohit mishra et al. has reported two stage networks that classifies the input X-ray into pneumonia and COVID-19. The F1 score is reported as 96.5% for stage 1 and 98.31% for stage 2. Further to that, precision and sensitivity are quantified as 96.04% & 97% respectively for non-pneumonic class and 100%, 96.74% respectively for Covid positive and 97.74%, 100% for covid-19 negative[13]. While in MdGulzar Hussain et al. results showed that the CNN model of three layers reported the highest precision, recall, and F1-score of 96%, 94%, and 96% respectively, compared to one layer gave the accuracy of 95% and four layer CNNs 94%[14]. In have implemented COVID-GATNet for combining dense blocks and the attention mechanism for reduced parameters and increased performance. Results claimed the “best-case accuracy of 94.1%, and the F-1 score with 95.2%” for COVID-GATNet as compared to COVID-Net model of 93.3%, and the F-1 score of 94.8% [15].

CT Scans

Harsh Sharma et.al. had evaluated the performance of a CNN network with and
without dropout layer along with data augmentation techniques. They have reported a
maximum accuracy of 90.6% and in the authors did a test of using various image pre-
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processing techniques. The observations reported for covid-19 diagnosis with accuracy
of 97.2% using CNN versus 98.5% for Support Vector Machine (SVM) [16].

**Other Diagnostic imaging**

Other sources like acoustics data of cough and breath sounds to train CNN, have studied the feasibility of using lung sounds and spectrograms for lung disease classification, to train on normalised and pre-processed sound recordings and spectrograms on 128x128 CNN model, Peak accuracy of 95% is reported [18].

5.2 Hybrid CNN

Hybrid CNNs are developed by taking advantage of CNN's layered structure. Phat Nguyen Kieu et.al. have implemented a hybrid CNN for diagnosis of abnormalities from chest X-rays by using two 2-D 64x128 CNN's for left and right lung X-ray and a 128x 128 CNN and then devising a method called fusion method for making a prediction of the abnormality reported with 96% accuracy [19].

5.3 Transfer Learning

For effective classification, there is a need for abundant data to enable effective
learning for classification. However, in many situations, availability of data is con-
strained. Transfer learning provides a way to deal with this limitation. Transfer learning
is the use of learning already learnt on one problem to solve a related problem.

Chest X-rays

In ResNet-50, Inception V3 and DenseNet121 models trained on the dataset of 112,120 images of chest X-rays are compared and densenet121 is reported for the best performance [20]. Another study reported CheXNet to outperform eight different CNN models. However with a bigger dataset, Densenet gave 2% more accuracy in [21]. The work has reported inception models the best with 98% accuracy against Visual Geometry Group (VGG)16, VGG19 and a basic CNN in [22]. The study has depicted an improved technique using knowledge transfer and distillation with Densenet-121 and lightweight squeezenet [23]. Additionally, an Xception for CXR classification and pre-processing like data augmentation and pixel intensity normalization were done to give 87% accuracy with AUC scores in ranges of 0.50 - 0.80 in [24]. D. Haritha et al. have achieved 98.5% accuracy with InceptionV1 training on 1824 Chest X-rays [25].

CT scans

The CT and X Ray images are rotated, zoomed, horizontally flipped and shifted
towards balancing the unbalanced dataset and Densenet-121 and Inception V3 are
shown to produce a performance increase by 12% [26].

Ultrasounds

TLum Et. al. have shown an improvement of 4 % and 10% of the POCVID-NET
[28] which reported 85% precision and 90% recall for COVID-19, using attention
mechanisms[27]. Similar attempts are also made by Julia Diaz-Escobar Et. al. with
transfer learning on ultrasound samples and reported that Inception v3 has reported best
accuracy of 89.3% [29].

5.4 Transfer learning with preprocessing

Md. Mehedi Hasan Et Al. had trained deep convolutional GAN for augmentation of synthetic images to supplement the dataset for the model to perform better. Then transfer learning was used with CNN by utilising VGG16 as the base model for image classification. The model achieved 94.5% accuracy on the validation set [30].

Ishan Mathew NedumkunnelEt. al. had implemented a CNN using transfer learning on pre-processed image dataset through Segmentation using a U-NET model. Out of Densenet201, EfficientNetB7 and VGG-16, DenseNet201 had performed the best [31].
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## 5.5 Data augmentations and Feature Extraction spatial domain and frequency domain

As real world data is limited, Data augmentation is used to diversify the training set by applying random real-like transformation, further feature extraction is done in frequency domain like Fast Fourier transform and Wavelet transform, and spatial domain like Grey level Difference Method (GLDM), Grey level Co-occurrence matrix (GLCM). Abolfazl Zargari Khuzani, Et. al. have designed a CNN model where feature extraction is done in both spatial domain, i.e. GLDM, GLCM and also in frequency domain i.e FFT and Wavelet. They tested the obtained dataset using a three layer model and the results with F1 scores of 0.98 for covid and 0.94 for pneumonia in [32]. Vladimir GROZA et al. had developed a robust method for covid detection with higher accuracy. They developed a semantic segmentation network on pneumothorax region using an ensemble of linknets with a backbone from se-resnext50, se-resnext101, SE-Net154, pre-processes image augmentation at localised regions when required instead of global random augmentation. They have shown a very good performance with an F1 of 0.8821 [33].

# 6 Generative adversarial Networks (GANs)

Generative adversarial Networks, or GAN, are DL models, commonly used for generating the images. In general, a GAN is composed of “generator (G) networks and discriminator (D) networks”. A generator model generates new artificial samples from an existing distribution of samples, and the discriminator is responsible for classifying whether samples are real or fake (generated).

## 6.1 Standard GANs

GANs can extract features from data and generate synthetic data that augment the limited dataset. Standard GANs consist of two neural networks as explained above working in tandem.

### For X-rays

Zhaohui Liang et al. had implemented Conditional GAN for Image Dataset augmentation and improved the distribution of the classes for better training of the model, as in the case of many medical datasets which tend to be highly imbalanced. The result reported an improvement from 96.1% to 97.8% and also with an increase in F1 scores, precision and recall [34].

### For CT scans

Martin Jammes-Floreani et al. tried to reduce the difficulties in implementation of CT scan pneumonia classification due to reduced image quality or resolution. The authors have utilized GAN for image enhancement using ESRGAN and TomoGAN adapted from SRGAN (Super Resolution GAN) [35].

## 6.2 Hybrid GANs

The popularity of GANs in computer vision has led us to looking for more avenues and ways to improve the GANs' performance and hence there are attempts to use a combination of convolutional neural networks and GANs and Vedant Bhagat. al. had also implemented the GAN as a mechanism of improving the quality of the dataset which can be used in turn to improve the performance of the model classifying for pneumonia detection using x-ray. They have used the Progressive GAN (ProGAN) for high-quality synthetic images generation. The DCNN AlexNet when applied on the augmented data set D2, produced an accuracy of 91%, while the accuracy was 79.5% when applied on the original dataset D1 [36]. Devansh Srivastava, Et. al. trained Deep convolutional GAN for augmentation of synthetic images to oversample the dataset for the model to perform better[37].
## 7 Long Short Term Memory Networks (LSTMs)

In a recurrent network, in addition to normal connection, a CNN has units that are self-connected or with previous layer units. This acts as a memory component and helps the network to 'remember'. The LSTM unit is a more complex form of the recurrent unit. It uses a gating unit to switch to choose between 'forget' or 'remember'. This also helps for LSTM to back propagate or in other words 'learn'.

A two-stage method to fully utilise cross-slice features of volumetric data by using a CNN and context aware Bi-LSTM, learned the dataset of 801 patients and shown an increased performance by 1-2% [38]. The cough recordings were analysed and diagnosing the underlying disease is implemented in [39]. LSTM is used over the preprocessed audio recordings with feature extraction of Mel-frequency cepstrum coefficients (MFCCs) in [40].

## 8 Auto Encoders for both CT and X-rays

Autoencoders are unsupervised neural networks which try to map the input data representation to output which results in a compressed version of the data thus effectively reducing the number of parameters required, it is used for image compression, denoising and generation.

A context based data extraction system is used with respect to diagnosing images and used for infecting source locating and spread monitoring using COVID-CHEST-XRay IEEE8023 dataset containing 760 images in [41]. An image retrieval system using Transfer learning and Denoising Auto encoder claims a precision of 0.436 in [42].

## 9 Reinforcement Learning

Reinforcement learning is a technique of AI like supervised and unsupervised learning, where a model is set up in an environment where it is tasked with taking decisions. Instead of using learning labels like the supervised reward system is in place where the model is rewarded for the favourable decision and the objective is to maximise the rewards obtained. Few papers have implemented the technique for diagnosis, An adapted version of Gaining-Sharing Knowledge (GSK) optimization algorithm using the Opposition-Based learning (OBL) and Cauchy mutation operators is reported in [43] to provide comparable gains. A swap learning methodology called salp swarm algorithm is reported with 98% accuracy and lowest running time in [44].

## 10 Regulatory framework

Industry regulators like International Medical Device Forum (IMDRF) have been consulted by the government regulators like the Federal Drug Administration (FDA) and European Medicine Agency (EMA) for development of a framework for regulating the marketing and introduction of AI software for categorising the devices and the action needed based on the risk present [45].

FDA has proposed a framework in January 2021 open for feedback and suggestions and proposed an action plan to increase transparency to users of AI/ML based devices. The EU have framework which deals with how medical device are classified based on risk, and which in turn denotes the testing and validation for testing and codified the requirements the medical devices manufacturer must demonstrate [46].

In India, The Ministry of Electronics and Information Technology formed a committee which has submitted a report to aid in the process of drafting an AI policy for India including healthcare.
# 11 Challenges and Future Research Directions

As promising as it sounds, the benefits offered by DL methods are not without ca-
veats. The availability of dataset is a primary concern, especially with respect to Indian
context. Though the datasets are widely available, the quality of datasets are to be eval-
uated. Many factors such as distribution, outlier identification, data cleaning and statis-
tical evaluation should be emphasized for more holistic results. Accuracy is one of the
heavily used metrics for citing the model's performance. However, accuracy may not
give a whole picture of the real time performance of the model. Other metrics such as
F1 score, AOC etc. can be employed to ensure that the models are more versatile for
real life scenario. As in the case of healthcare domain, any innovation using DL meth-
ods should be strictly evaluated before public use. Many countries have already began
work in framing protocols for the above purpose, creation of a common standard will
aid in more assurance of public trust during real time implementation. Cross validation
as with human evaluators can be employed as a benchmarking metric, though the cost
and legal factors have also to be considered.

12 Conclusion

This paper surveys DL techniques implemented as a means of providing tools for
rapid diagnosis and access to treatment for managing COVID-19 pandemic along with
listing out available regulatory structures present. This DL based development is largely
supported by the ease of access of literature, open datasets and tools. At SAMEER
design and implementation, efforts are focusing on advanced statistical techniques, to-
wards building optimized and reliable DL models for healthcare and industrial use
cases.

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4. Cheng, JZ. Ni, YH. et al. Computer-Aided Diagnosis with DL Architecture: Applications to Breast Lesions in US Images and Pulmonary Nodules in CT Scans. Sci Rep 6, 24454 (2016). https://doi.org/10.1038/srep24454

5. Lakshmanaprabu S.K. et al. Optimal DL model for classification of lung cancer on CT images, Future Generation Computer Systems, vol.92, pp 374-382, Science Direct (2018). https://doi.org/10.1016/j.future.2018.10.009

6. Sadre, R., Sundaram, B., Majumdar, S. et al. Validating DL inference during chest X-ray classification for COVID-19 screening. Sci Rep 11, 16075 (2021).

7. Classification: ROC Curve and AUC, https://developers.google.com/machine-learn-
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9. Althnian, AlSaeed, Al-Baity et al., Impact of Dataset Size on Classification Performance: An Empirical Evaluation in the Medical Domain. Appl. Sci. 2021, 11, 796

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