pt-sk/ml_research_abs huggingface dataset:
Lightweight deep CNN-based models for early detection of COVID-19 patients from chest X-ray images.
Authors: Haval IHussein, Abdulhakeem OMohammed, Masoud MHassan, Ramadhan JMstafa
Journal: Expert systems with applications

Hundreds of millions of people worldwide have recently been infected by the novel Coronavirus disease (COVID-19), causing significant damage to the health, economy, and welfare of the world's population. Moreover, the unprecedented number of patients with COVID-19 has placed a massive burden on healthcare centers, making timely and rapid diagnosis challenging. A crucial step in minimizing the impact of such problems is to automatically detect infected patients and place them under special care as quickly as possible. Deep learning algorithms, such as Convolutional Neural Networks (CNN), can be used to meet this need. Despite the desired results, most of the existing deep learning-based models were built on millions of parameters (weights), which are not applicable to devices with limited resources. Inspired by such fact, in this research, we developed two new lightweight CNN-based diagnostic models for the automatic and early detection of COVID-19 subjects from chest X-ray images. The first model was built for binary classification (COVID-19 and Normal), whereas the second one was built for multiclass classification (COVID-19, viral pneumonia, or normal). The proposed models were tested on a relatively large dataset of chest X-ray images, and the results showed that the accuracy rates of the 2- and 3-class-based classification models are 98.55% and 96.83%, respectively. The results also revealed that our models achieved competitive performance compared with the existing heavyweight models while significantly reducing cost and memory requirements for computing resources. With these findings, we can indicate that our models are helpful to clinicians in making insightful diagnoses of COVID-19 and are potentially easily deployable on devices with limited computational power and resources.
MTMC-AUR2CNet: Multi-textural multi-class attention recurrent residual convolutional neural network for COVID-19 classification using chest X-ray images.
Authors: AnandbabuGopatoti, PVijayalakshmi
Journal: Biomedical signal processing and control

Coronavirus disease (COVID-19) has infected over 603 million confirmed cases as of September 2022, and its rapid spread has raised concerns worldwide. More than 6.4 million fatalities in confirmed patients have been reported. According to reports, the COVID-19 virus causes lung damage and rapidly mutates before the patient receives any diagnosis-specific medicine. Daily increasing COVID-19 cases and the limited number of diagnosis tool kits encourage the use of deep learning (DL) models to assist health care practitioners using chest X-ray (CXR) images. The CXR is a low radiation radiography tool available in hospitals to diagnose COVID-19 and combat this spread. We propose a Multi-Textural Multi-Class (MTMC) UNet-based Recurrent Residual Convolutional Neural Network (MTMC-UR2CNet) and MTMC-UR2CNet with attention mechanism (MTMC-AUR2CNet) for multi-class lung lobe segmentation of CXR images. The lung lobe segmentation output of MTMC-UR2CNet and MTMC-AUR2CNet are mapped individually with their input CXRs to generate the region of interest (ROI). The multi-textural features are extracted from the ROI of each proposed MTMC network. The extracted multi-textural features from ROI are fused and are trained to the Whale optimization algorithm (WOA) based DeepCNN classifier on classifying the CXR images into normal (healthy), COVID-19, viral pneumonia, and lung opacity. The experimental result shows that the MTMC-AUR2CNet has superior performance in multi-class lung lobe segmentation of CXR images with an accuracy of 99.47%, followed by MTMC-UR2CNet with an accuracy of 98.39%. Also, MTMC-AUR2CNet improves the multi-textural multi-class classification accuracy of the WOA-based DeepCNN classifier to 97.60% compared to MTMC-UR2CNet.
PDAtt-Unet: Pyramid Dual-Decoder Attention Unet for Covid-19 infection segmentation from CT-scans.
Authors: FaresBougourzi, CosimoDistante, FadiDornaika, AbdelmalikTaleb-Ahmed
Journal: Medical image analysis

Since the emergence of the Covid-19 pandemic in late 2019, medical imaging has been widely used to analyze this disease. Indeed, CT-scans of the lungs can help diagnose, detect, and quantify Covid-19 infection. In this paper, we address the segmentation of Covid-19 infection from CT-scans. To improve the performance of the Att-Unet architecture and maximize the use of the Attention Gate, we propose the PAtt-Unet and DAtt-Unet architectures. PAtt-Unet aims to exploit the input pyramids to preserve the spatial awareness in all of the encoder layers. On the other hand, DAtt-Unet is designed to guide the segmentation of Covid-19 infection inside the lung lobes. We also propose to combine these two architectures into a single one, which we refer to as PDAtt-Unet. To overcome the blurry boundary pixels segmentation of Covid-19 infection, we propose a hybrid loss function. The proposed architectures were tested on four datasets with two evaluation scenarios (intra and cross datasets). Experimental results showed that both PAtt-Unet and DAtt-Unet improve the performance of Att-Unet in segmenting Covid-19 infections. Moreover, the combination architecture PDAtt-Unet led to further improvement. To Compare with other methods, three baseline segmentation architectures (Unet, Unet++, and Att-Unet) and three state-of-the-art architectures (InfNet, SCOATNet, and nCoVSegNet) were tested. The comparison showed the superiority of the proposed PDAtt-Unet trained with the proposed hybrid loss (PDEAtt-Unet) over all other methods. Moreover, PDEAtt-Unet is able to overcome various challenges in segmenting Covid-19 infections in four datasets and two evaluation scenarios.
MESTrans: Multi-scale embedding spatial transformer for medical image segmentation.
Authors: YatongLiu, YuZhu, YingXin, YananZhang, DaweiYang, TaoXu
Journal: Computer methods and programs in biomedicine

Transformers profiting from global information modeling derived from the self-attention mechanism have recently achieved remarkable performance in computer vision. In this study, a novel transformer-based medical image segmentation network called the multi-scale embedding spatial transformer (MESTrans) was proposed for medical image segmentation.
First, a dataset called COVID-DS36 was created from 4369 computed tomography (CT) images of 36 patients from a partner hospital, of which 18 had COVID-19 and 18 did not. Subsequently, a novel medical image segmentation network was proposed, which introduced a self-attention mechanism to improve the inherent limitation of convolutional neural networks (CNNs) and was capable of adaptively extracting discriminative information in both global and local content. Specifically, based on U-Net, a multi-scale embedding block (MEB) and multi-layer spatial attention transformer (SATrans) structure were designed, which can dynamically adjust the receptive field in accordance with the input content. The spatial relationship between multi-level and multi-scale image patches was modeled, and the global context information was captured effectively. To make the network concentrate on the salient feature region, a feature fusion module (FFM) was established, which performed global learning and soft selection between shallow and deep features, adaptively combining the encoder and decoder features. Four datasets comprising CT images, magnetic resonance (MR) images, and H&E-stained slide images were used to assess the performance of the proposed network.
Experiments were performed using four different types of medical image datasets. For the COVID-DS36 dataset, our method achieved a Dice similarity coefficient (DSC) of 81.23%. For the GlaS dataset, 89.95% DSC and 82.39% intersection over union (IoU) were obtained. On the Synapse dataset, the average DSC was 77.48% and the average Hausdorff distance (HD) was 31.69 mm. For the I2CVB dataset, 92.3% DSC and 85.8% IoU were obtained.
The experimental results demonstrate that the proposed model has an excellent generalization ability and outperforms other state-of-the-art methods. It is expected to be a potent tool to assist clinicians in auxiliary diagnosis and to promote the development of medical intelligence technology.
COVID-19 diagnosis: A comprehensive review of pre-trained deep learning models based on feature extraction algorithm.
Authors: Rahul GowthamPoola, LahariPl, Siva SankarY
Journal: Results in engineering

Due to the augmented rise of COVID-19, clinical specialists are looking for fast faultless diagnosis strategies to restrict Covid spread while attempting to lessen the computational complexity. In this way, swift diagnosis techniques for COVID-19 with high precision can offer valuable aid to clinical specialists. RT- PCR test is an expensive and tedious COVID diagnosis technique in practice. Medical imaging is feasible to diagnose COVID-19 by X-ray chest radiography to get around the shortcomings of RT-PCR. Through a variety of Deep Transfer-learning models, this research investigates the potential of Artificial Intelligence -based early diagnosis of COVID-19 via X-ray chest radiographs. With 10,192 normal and 3616 Covid X-ray chest radiographs, the deep transfer-learning models are optimized to further the accurate diagnosis. The x-ray chest radiographs undergo a data augmentation phase before developing a modified dataset to train the Deep Transfer-learning models. The Deep Transfer-learning architectures are trained using the extracted features from the Feature Extraction stage. During training, the classification of X-ray Chest radiographs based on feature extraction algorithm values is converted into a feature label set containing the classified image data with a feature string value representing the number of edges detected after edge detection. The feature label set is further tested with the SVM, KNN, NN, Naive Bayes and Logistic Regression classifiers to audit the quality metrics of the proposed model. The quality metrics include accuracy, precision, F1 score, recall and AUC. The Inception-V3 dominates the six Deep Transfer-learning models, according to the assessment results, with a training accuracy of 84.79% and a loss function of 2.4%. The performance of Cubic SVM was superior to that of the other SVM classifiers, with an AUC score of 0.99, precision of 0.983, recall of 0.8977, accuracy of 95.8%, and F1 score of 0.9384. Cosine KNN fared better than the other KNN classifiers with an AUC score of 0.95, precision of 0.974, recall of 0.777, accuracy of 90.8%, and F1 score of 0.864. Wide NN fared better than the other NN classifiers with an AUC score of 0.98, precision of 0.975, recall of 0.907, accuracy of 95.5%, and F1 score of 0.939. According to the findings, SVM classifiers topped other classifiers in terms of performance indicators like accuracy, precision, recall, F1-score, and AUC. The SVM classifiers reported better mean optimal scores compared to other classifiers. The performance assessment metrics uncover that the proposed methodology can aid in preliminary COVID diagnosis.
COVID-19 and pneumonia diagnosis from chest X-ray images using convolutional neural networks.
Authors: MuhabHariri, ErcanAvşar
Journal: Network modeling and analysis in health informatics and bioinformatics

X-ray is a useful imaging modality widely utilized for diagnosing COVID-19 virus that infected a high number of people all around the world. The manual examination of these X-ray images may cause problems especially when there is lack of medical staff. Usage of deep learning models is known to be helpful for automated diagnosis of COVID-19 from the X-ray images. However, the widely used convolutional neural network architectures typically have many layers causing them to be computationally expensive. To address these problems, this study aims to design a lightweight differential diagnosis model based on convolutional neural networks. The proposed model is designed to classify the X-ray images belonging to one of the four classes that are Healthy, COVID-19, viral pneumonia, and bacterial pneumonia. To evaluate the model performance, accuracy, precision, recall, and F1-Score were calculated. The performance of the proposed model was compared with those obtained by applying transfer learning to the widely used convolutional neural network models. The results showed that the proposed model with low number of computational layers outperforms the pre-trained benchmark models, achieving an accuracy value of 89.89% while the best pre-trained model (Efficient-Net B2) achieved accuracy of 85.7%. In conclusion, the proposed lightweight model achieved the best overall result in classifying lung diseases allowing it to be used on devices with limited computational power. On the other hand, all the models showed a poor precision on viral pneumonia class and confusion in distinguishing it from bacterial pneumonia class, thus a decrease in the overall accuracy.
Computed tomography-based COVID-19 triage through a deep neural network using mask-weighted global average pooling.
Authors: Hong-TaoZhang, Ze-YuSun, JuanZhou, ShenGao, Jing-HuiDong, YuanLiu, XuBai, Jin-LinMa, MingLi, GuangLi, Jian-MingCai, Fu-GengSheng
Journal: Frontiers in cellular and infection microbiology

There is an urgent need to find an effective and accurate method for triaging coronavirus disease 2019 (COVID-19) patients from millions or billions of people. Therefore, this study aimed to develop a novel deep-learning approach for COVID-19 triage based on chest computed tomography (CT) images, including normal, pneumonia, and COVID-19 cases.
A total of 2,809 chest CT scans (1,105 COVID-19, 854 normal, and 850 non-3COVID-19 pneumonia cases) were acquired for this study and classified into the training set (n = 2,329) and test set (n = 480). A U-net-based convolutional neural network was used for lung segmentation, and a mask-weighted global average pooling (GAP) method was proposed for the deep neural network to improve the performance of COVID-19 classification between COVID-19 and normal or common pneumonia cases.
The results for lung segmentation reached a dice value of 96.5% on 30 independent CT scans. The performance of the mask-weighted GAP method achieved the COVID-19 triage with a sensitivity of 96.5% and specificity of 87.8% using the testing dataset. The mask-weighted GAP method demonstrated 0.9% and 2% improvements in sensitivity and specificity, respectively, compared with the normal GAP. In addition, fusion images between the CT images and the highlighted area from the deep learning model using the Grad-CAM method, indicating the lesion region detected using the deep learning method, were drawn and could also be confirmed by radiologists.
This study proposed a mask-weighted GAP-based deep learning method and obtained promising results for COVID-19 triage based on chest CT images. Furthermore, it can be considered a convenient tool to assist doctors in diagnosing COVID-19.
Deep SVDD and Transfer Learning for COVID-19 Diagnosis Using CT Images.
Authors: Akram AAlhadad, Reham RMostafa, Hazem MEl-Bakry
Journal: Computational intelligence and neuroscience

The novel coronavirus disease (COVID-19), which appeared in Wuhan, China, is spreading rapidly worldwide. Health systems in many countries have collapsed as a result of this pandemic, and hundreds of thousands of people have died due to acute respiratory distress syndrome caused by this virus. As a result, diagnosing COVID-19 in the early stages of infection is critical in the fight against the disease because it saves the patient's life and prevents the disease from spreading. In this study, we proposed a novel approach based on transfer learning and deep support vector data description (DSVDD) to distinguish among COVID-19, non-COVID-19 pneumonia, and intact CT images. Our approach consists of three models, each of which can classify one specific category as normal and the other as anomalous. To our knowledge, this is the first study to use the one-class DSVDD and transfer learning to diagnose lung disease. For the proposed approach, we used two scenarios: one with pretrained VGG16 and one with ResNet50. The proposed models were trained using data gathered with the assistance of an expert radiologist from three internet-accessible sources in end-to-end fusion using three split data ratios. Based on training with 70%, 50%, and 30% of the data, the proposed VGG16 models achieved (0.8281, 0.9170, and 0.9294) for the F1 score, while the proposed ResNet50 models achieved (0.9109, 0.9188, and 0.9333).
CCTCOVID: COVID-19 detection from chest X-ray images using Compact Convolutional Transformers.
Authors: AbdolrezaMarefat, MahdiehMarefat, JavadHassannataj Joloudari, Mohammad AliNematollahi, RezaLashgari
Journal: Frontiers in public health

COVID-19 is a novel virus that attacks the upper respiratory tract and the lungs. Its person-to-person transmissibility is considerably rapid and this has caused serious problems in approximately every facet of individuals' lives. While some infected individuals may remain completely asymptomatic, others have been frequently witnessed to have mild to severe symptoms. In addition to this, thousands of death cases around the globe indicated that detecting COVID-19 is an urgent demand in the communities. Practically, this is prominently done with the help of screening medical images such as Computed Tomography (CT) and X-ray images. However, the cumbersome clinical procedures and a large number of daily cases have imposed great challenges on medical practitioners. Deep Learning-based approaches have demonstrated a profound potential in a wide range of medical tasks. As a result, we introduce a transformer-based method for automatically detecting COVID-19 from X-ray images using Compact Convolutional Transformers (CCT). Our extensive experiments prove the efficacy of the proposed method with an accuracy of 99.22% which outperforms the previous works.
IRCM-Caps: An X-ray image detection method for COVID-19.
Authors: ShuoQiu, JinlinMa, ZipingMa
Journal: The clinical respiratory journal

COVID-19 is ravaging the world, but traditional reverse transcription-polymerase reaction (RT-PCR) tests are time-consuming and have a high false-negative rate and lack of medical equipment. Therefore, lung imaging screening methods are proposed to diagnose COVID-19 due to its fast test speed. Currently, the commonly used convolutional neural network (CNN) model requires a large number of datasets, and the accuracy of the basic capsule network for multiple classification is limital. For this reason, this paper proposes a novel model based on CNN and CapsNet.
The proposed model integrates CNN and CapsNet. And attention mechanism module and multi-branch lightweight module are applied to enhance performance. Use the contrast adaptive histogram equalization (CLAHE) algorithm to preprocess the image to enhance image contrast. The preprocessed images are input into the network for training, and ReLU was used as the activation function to adjust the parameters to achieve the optimal.
The test dataset includes 1200 X-ray images (400 COVID-19, 400 viral pneumonia, and 400 normal), and we replace CNN of VGG16, InceptionV3, Xception, Inception-Resnet-v2, ResNet50, DenseNet121, and MoblieNetV2 and integrate with CapsNet. Compared with CapsNet, this network improves 6.96%, 7.83%, 9.37%, 10.47%, and 10.38% in accuracy, area under the curve (AUC), recall, and F1 scores, respectively. In the binary classification experiment, compared with CapsNet, the accuracy, AUC, accuracy, recall rate, and F1 score were increased by 5.33%, 5.34%, 2.88%, 8.00%, and 5.56%, respectively.
The proposed embedded the advantages of traditional convolutional neural network and capsule network and has a good classification effect on small COVID-19 X-ray image dataset.
Implementation of deep learning artificial intelligence in vision-threatening disease screenings for an underserved community during COVID-19.
Authors: ArethaZhu, PriyaTailor, RashikaVerma, IsisZhang, BrianSchott, CatherineYe, BernardSzirth, MiriamHabiel, Albert SKhouri
Journal: Journal of telemedicine and telecare

Age-related macular degeneration, diabetic retinopathy, and glaucoma are vision-threatening diseases that are leading causes of vision loss. Many studies have validated deep learning artificial intelligence for image-based diagnosis of vision-threatening diseases. Our study prospectively investigated deep learning artificial intelligence applications in student-run non-mydriatic screenings for an underserved, primarily Hispanic community during COVID-19.
Five supervised student-run community screenings were held in West New York, New Jersey. Participants underwent non-mydriatic 45-degree retinal imaging by medical students. Images were uploaded to a cloud-based deep learning artificial intelligence for vision-threatening disease referral. An on-site tele-ophthalmology grader and remote clinical ophthalmologist graded images, with adjudication by a senior ophthalmologist to establish the gold standard diagnosis, which was used to assess the performance of deep learning artificial intelligence.
A total of 385 eyes from 195 screening participants were included (mean age 52.43  ±  14.5 years, 40.0% female). A total of 48 participants were referred for at least one vision-threatening disease. Deep learning artificial intelligence marked 150/385 (38.9%) eyes as ungradable, compared to 10/385 (2.6%) ungradable as per the human gold standard (
Deep learning artificial intelligence can increase the efficiency and accessibility of vision-threatening disease screenings, particularly in underserved communities. Deep learning artificial intelligence should be adaptable to different environments. Consideration should be given to how deep learning artificial intelligence can best be utilized in a real-world application, whether in computer-aided or autonomous diagnosis.