key: cord-0303470-r9oa9gm6
authors: Ali, Hazrat; Shah, Zubair
title: Combating COVID-19 using Generative Adversarial Networks and Artificial Intelligence for Medical Images: A Scoping Review
date: 2022-05-15
journal: nan
DOI: 10.2196/37365
sha: 5d54a58aa726af2223824234c8d01532058d0f13
doc_id: 303470
cord_uid: r9oa9gm6

This review presents a comprehensive study on the role of GANs in addressing the challenges related to COVID-19 data scarcity and diagnosis. It is the first review that summarizes the different GANs methods and the lungs images datasets for COVID-19. It attempts to answer the questions related to applications of GANs, popular GAN architectures, frequently used image modalities, and the availability of source code. This review included 57 full-text studies that reported the use of GANs for different applications in COVID-19 lungs images data. Most of the studies (n=42) used GANs for data augmentation to enhance the performance of AI techniques for COVID-19 diagnosis. Other popular applications of GANs were segmentation of lungs and super-resolution of the lungs images. The cycleGAN and the conditional GAN were the most commonly used architectures used in nine studies each. 29 studies used chest X-Ray images while 21 studies used CT images for the training of GANs. For majority of the studies (n=47), the experiments were done and results were reported using publicly available data. A secondary evaluation of the results by radiologists/clinicians was reported by only two studies. Conclusion: Studies have shown that GANs have great potential to address the data scarcity challenge for lungs images of COVID-19. Data synthesized with GANs have been helpful to improve the training of the Convolutional Neural Network (CNN) models trained for the diagnosis of COVID-19. Besides, GANs have also contributed to enhancing the CNNs performance through the super-resolution of the images and segmentation. This review also identified key limitations of the potential transformation of GANs based methods in clinical applications.

In December 2019, the COVID-19 broke out and spread at an unprecedented rate, given the highly contagious nature of the virus. As a result, the World Health Organization (WHO) declared it a global pandemic in March 2020 [1] . Therefore, a response to combat the spread through speedy diagnosis became the most critical need of the time. A common method for diagnosing COVID-19 is the use of a realtime reverse transcription-polymerase chain reaction (RT-PCR) test. However, with the increasing number of cases worldwide, the health care sector was overloaded as it became challenging to cope with the requirements of the tests with the available testing facilities. Besides, research studies showed that RT-PCR may result in false negatives or fluctuating results [2] . Hence, diagnosis through computed tomography (CT) and X-Ray images of lungs may supplement the performance. Motivated by this need, alternative methods such as automatic diagnosis of COVID-19 from lungs images were explored and encouraged. In this regard, it is well understood that Artificial Intelligence (AI) techniques could help inspect chest CTs and X-rays within seconds and augment the public health care sector. The use of properly trained AI models for diagnosis of COVID-19 is promising for scaling up the capacity and requires training of the CNN with a large number of chest X-Ray images both for COVID-19 and normal cases. Since the diagnosis of COVID-19 requires studying of lungs CT or X-Ray images, the availability of lungs imaging data is vital to develop medical imaging methods. However, the lack of data for COVID-19 hampered the initial progress on developing these methods to combat COVID-19.

Many early attempts were made to collect image data for lungs infected with COVID-19 -specifically CT and X-Ray images either through a private collection in hospitals or through crowdsourcing using public platforms. In parallel to this, many studies explored the use of Generative Adversarial Networks (GANs) to generate synthetic image data that can improve the training of AI models to diagnose COVID-19.

GANs are a family of deep learning models that consist of two neural networks trained in an adversarial fashion [8] [9] [10] [11] [12] [13] [14] [15] . The two neural networks, namely the generator and the discriminator, attempt to minimize their losses while maximizing the loss of the other. This training mechanism improves the overall learning task of the GAN model, particularly for generating data. GANs have recently been studied for computer vision and medical imaging tasks such as image generation, super-This is a pre-print of the paper accepted for publication in JMIR Medical Informatics. Final version will be available from JMIR. doi: 10.2196/37365 resolution, and segmentation [9, 10] . Given the significant potential of GANs in medical imaging, it was intuitive that many researchers were tempted to explore the use of GANs for data augmentation of imaging data for COVID- 19 . In addition, some researchers also used GANs for segmentation and super-resolution of lungs images.

This scoping review focuses on providing a comprehensive review of the GANs based methods used to combat COVID-19. Specifically, it covers the studies where GANs have been used for lungs CT and X-Ray images to diagnose COVID-19 or to enhance the performance of CNNs for the diagnosis of COVID-19 (for example, by data augmentation or super-resolution).

GANs have gained the attention of the medical imaging research community. As the COVID-19 pandemic continued to grow in 2020 and 2021, the research community faced a significant challenge due to the scarcity of medical image data on COVID-19 that can be used to train AI models (for example, CNN) to perform COVID-19 diagnosis automatically. Given the popularity of GANs for image synthesis, researchers turned to exploring the use of GANs for data augmentation of lungs radiology images. Many studies were conducted to use different variants of GANs for data augmentation of lungs CT images and lungs X-Ray images. Similarly, few studies also used GANs for the diagnosis of COVID-19 from lungs radiology images.

However, to the best of our knowledge, there is no review to study the role of GANs in addressing the challenges related to COVID-19 data scarcity and diagnosis. The following research questions related to COVID-19 image data were considered for this review. 6. What studies were conducted and presented to radiology experts for evaluation of the suitability towards future use in clinical applications?

The results of this review will be helpful for researchers and professionals in the medical imaging and healthcare domain who are considering using GANs methods to address challenges related to COVID-19 imaging data and to address the challenge of improving the automatic diagnosis using radiology images.

In this work, a scoping review was conducted following the guidelines of "Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews" (PRISMA-ScR) [16] . The methods for performing the study are described below.

A search was conducted between 11 October to 13 October 2021. The search was performed on the following five databases: Pubmed, IEEEXplore, ACM Digital This is a pre-print of the paper accepted for publication in JMIR Medical Informatics. Final version will be available from JMIR. doi: 10.2196/37365 Library, Scopus, and Google Scholar. In the case of Google Scholar, only the first 99 results were retained as the results beyond 99 items were highly irrelevant to the scope of the study. Similarly, in the case of ACM Digital Library, the first 100 results were retained as a lack of relevancy to the study was obvious in results beyond 100.

The search terms used in this study were chosen from the literature with guidance from experts in the field. The terms were chosen based on the intervention (for example, generative adversarial networks, GANs, cycleGANs) and the target application (COVID-19, coronavirus, corona pandemic). The exact search strings used in the search for this study are available in Appendix 1.

This study focused on the applications of GANs in radiology images of lungs for COVID-19 used for any purpose such as data augmentation or synthesis, diagnosis, super-resolution, and prognosis. Only those studies were included that reported GANs based methods for chest x-ray images, chest CT images and chest ultrasound images. Studies that reported GANs based methods for non-lungs images were removed. Any studies that used deep learning methods but did not use GANs were also excluded. Studies reporting GANs for non-image data were also excluded. To provide a list of reliable studies, only peer-reviewed articles, conference papers and book chapters were included. Preprints, conference abstracts, short letters, and commentaries were excluded. Similarly, review articles were also excluded. No restrictions were imposed on the country of publication, study design, or outcomes. This is a pre-print of the paper accepted for publication in JMIR Medical Informatics. Final version will be available from JMIR. doi: 10.2196/37365 Studies that were written in English and were published from 2020 to 2022 were included. No studies before 2020 were included.

Two reviewers namely authors HA and ZS screened the titles and abstracts of the search results. Initial screening by the two reviewers was performed independently.

The disagreement occurred for 9 articles only. The disagreement was resolved through mutual discussion and consensus. For measuring the disagreement, Cohen

Kappa [17] was calculated to be 0.89, which shows good agreement between the two independent reviewers. Appendix 2 shows the matrix for the agreement between the two independent reviewers.

Appendix 3 shows the form for extraction of the key characteristics. The form was pilot-tested and refined in two rounds, firstly by data extraction for five studies and then by data extraction for another five studies. This refinement of the form ensured that only relevant data is extracted from the studies. The two reviewers (HA and ZS) extracted the data from the included studies, related to the GANs-based method, applications, and data sets. Any disagreement between the reviewers was resolved through mutual consensus and discussions. As the disagreements at the study selection stage were resolved through careful and lengthy discussions, the disagreement at the data extraction was only minor.

After extraction of the data from the full text of the identified studies, a narrative approach was used to synthesize the data. The use of GANs methods was classified This is a pre-print of the paper accepted for publication in JMIR Medical Informatics. Final version will be available from JMIR. doi: 10.2196/37365 in terms of the application of GAN (for example, augmentation, segmentation of lungs, etc.), the type of GAN architecture, if reported (for example, conditional GAN or cycleGAN), and the modality of the imaging data for which the GAN was used (for example, CT or X-Ray imaging). Similarly, the studies were classified based on the availability of the dataset (for example, public or private), the size of the dataset (for example, the number of images in the original images and the number of images after augmentation with GAN, if applicable), and the proportion of the training and test sets as well as the type of cross-validation. The data synthesis was managed and performed using Microsoft Excel (Microsoft Corporation) workbook. This is a pre-print of the paper accepted for publication in JMIR Medical Informatics. Final version will be available from JMIR. doi: 10.2196/37365

From five online databases, a total of 348 studies were retrieved (see Figure 1 ). Out (Table 1) .

( Figure 2 ) shows the demographics of the included studies along with the modality of the chest images used. 

As shown in (Table 2) , the included studies have reported five different tasks being addressed: augmentation (data augmentation), diagnosis of COVID-19, prognosis, segmentation (to identify lungs region), and diagnosis of lungs disease. As the diagnosis of COVID-19 using medical imaging has been a priority since the pandemic started, 39 out of 57 studies [19] [20] [21] , [23 -33] , [35 -37] , [39] , [41, 42] , [44] , [46] , [50] , [52] , [53] , [55] , [56] , [58 -60] , [63 -69] , [71, 72] have reported the diagnosis of COVID-19 as the main focus of their works. Nine studies [18] , [43] , [45] , [49] , Figure 2 . Characteristics of the included studies showing the publication type, country of publication, the modality of data. The number of studies is reflected by the size of terminal node. The numbers S1 to S57 refer to the included studies, as per (18) through (74) in the references.

This is a pre-print of the paper accepted for publication in JMIR Medical Informatics. Final version will be available from JMIR. doi: 10.2196/37365 [54] , [61, 62] reported data augmentation as the main task addressed in the work. In addition, one study [22] reported prognosis of COVID-19 disease, three studies reported segmentation of lungs [34] , [51] , [57] , one study reported diagnosis of multiple lungs diseases [47] .

The majority of the studies used GANs to augment the data where they reported the use of GANs to increase the dataset size. Specifically, 42 studies [18] , [21] , [23] , [24 -29] , [31 -36] , [38 -43] , [45] , [46] , [48] , [50] , [52 -56] , [59 -67] , [71] , [73, 74] used GANs based methods for data augmentation. The augmented data were then used to improve the training of different CNNs to diagnose COVID-19. Three studies [37], [51] , [57] used GANs for segmentation of the lungs region within the chest radiology images. Three studies [30] , [44] , [68] used GANs for super-resolution to improve the quality of the images before using the images for diagnosis purposes. Five studies [20] , [58] , [69, 70] , [72] used GANs for the diagnosis of COVID-19. Two studies [19, 47] used GANs for features extraction from images and one study [22] used GANs method for prognosis of the COVID-19 disease. The prevalent mode of imaging is the use of 2D image data and one study reported GANs based method for synthesizing 3D data [49] . (Figure 3 ) shows the mapping of the applications of GANs based methods for all the included studies. The most common type of GAN used in these studies was the cycleGAN, used in 9

studies [29] , [35, 36] , [42] , [46] , [54] , [56] , [70] , [74] . The cycleGAN is an image translation GAN that does not require paired data to transform images from one domain to another. Other popular types of GANs were conditional GAN used by nine studies [18] , [22] , [24, 25] , [33] , [37], [41] , [57] , [60] , Deep Convolutional GAN used by four studies [21] , [38] , [43] , [67] , and auxiliary classifier GAN used by four studies [32] , [40] , [55] , [69] . The super-resolution GAN was used by two studies [44] , [68] . One study [31] reported the use of multiple GANs namely, Wassertein Out of 57 studies, only ten studies [18, 19] , [26, 27] , [30] , [34] , [43] , [61 -73] reported changes to the architecture of the GAN they were using. For the rest of the studies, no major changes were reported to the architecture of the GAN.

The included studies have applied GANs on lungs radiology images of various modalities. Specifically, the use of X-Ray images dominated the studies. 29 studies [20, 21] , [25] , [27 -29] , [31, 32] , [35] , [37] , [40 -43] , [45] , [50] , [52] , [54] , [56] , [57] , [59, 60] , [62] , [64, 65] , [67] , [70] , [73, 74] used X-Ray images of lungs while 21 studies [18, 19] , [22 -24] , [26] , [30] , [33, 34] , [36] , [38] , [48, 49] , [51] , [53] , [55] , [58] , [61] , [63] , [66] , [71] used CT images. Six studies [39] , [44] , [46] , [47] , [68] , [72] reported the use of both the X-Ray and CT images. Only one study [69] used ultrasound images for COVID-19 diagnosis which shows that the ultrasound is not a popular imaging modality for training GANs and other deep learning models for COVID-19 detection (also see Figure 4 ). Out of the 57 studies, most of the studies (n=47) have used images data sets that are publicly available. For ten studies, the data sets used in the work are private. (Table 3) Majority of the studies reported the size of the data set in terms of the number of images. The number of images used was greater than 10,000 in only seven studies This is a pre-print of the paper accepted for publication in JMIR Medical Informatics. Final version will be available from JMIR. doi: 10.2196/37365 [20] , [22] , [30] , [39] , [63] , [66] , [74] . Three studies used images between 5000 and 10,000 [33] , [47] , [64] . The most common range for the number of images used was between 1000 to 5000 images used in 15 studies. Around one-fifth of the studies (n=11) used the number of images between 500 and 1000. In 11 studies, the number of images used was less than 500. No study reported a number of images less than 100. The maximum number of images was 84971 used by [22] . Only a few of the studies reported the number of patients for whom the data has been used.

One study [26] used data for more than 1000 patients. Two studies [29] , [42] used data for 500 to 1000 patients. Six studies [19] , [22] , [24] , [30] , [38] , [71] used data for 100 to 500 patients. Four studies [18] , [49] , [66] , [69] used data for less than 100

patients. The number of patients was not reported in the rest of the studies.

After augmentation using GANs, the studies have increased the number of images to several thousand with a maximum number of 21295 [54] . In six studies using GANs for data augmentation, the number of images was increased to more than 10,000. In three studies, the number of images was increased between 5000 to 10,000. In nine studies, the increased number of images was between 1000 to 5000 and in two studies, the increased number of images was between 500 and 1000. No study reported data augmentation output below 500 images.

This is a pre-print of the paper accepted for publication in JMIR Medical Informatics. Final version will be available from JMIR. doi: 10.2196/37365

Generally, the popular metrics for evaluating the diagnosis and classification performances for neural networks are accuracy, precision, recall, dice score, and area under the ROC curve. To evaluate the performance of diagnosis of COVID-19, 38 studies used accuracy along with metrics like precision, recall and dice score [21] , [23 -28] , [31 -34] , [36] , [38] , [40] , [43 -48] , [52, 53] , [55] , [56] , [58 -60] , [63 -72] , [74] . Around one-fourth of the studies (n=18) used sensitivity and specificity.

12 studies used AUC [19] , [20] , [26] , [30] , [32] , [46] , [47] , [48] , [50] , [51] , [68] , [74] .

The numbers do not add up as many studies used more than one metric for evaluation. Besides the metrics mentioned above, only one study [22] used additional metrics, namely concordance index and relative absolute error, to evaluate prognosis and survival prediction for COVID-19 affected individuals.

Likewise, the popular metrics to assess the quality of the synthesized images are SSIM, PSNR and FID. In the included studies, six studies used the SSIM metric [18] , [30] , [49] , [60] [61] [62] , five used PSNR [18] , [30] , [49] , [61, 62] and three used the FID metric for evaluation [18] , [43] , [62] . 

This review also summarizes the studies for which the authors provided the implementation code. Only seven [19, 20] , [34] , [47, 48] , [66] , [70] out of the 57 studies provided links for their code. Only two studies [19, 45] reported a secondary evaluation by radiologists/doctors/experts by presenting the outcome of the results obtained by their model. One study [19] presented their results of an end-to-end diagnosis COVID-19 from CT images to three radiologists for a second opinion. One study [45] presented the synthetic X-Ray images to two radiologists for a second opinion on the quality of the generated X-Ray images. In the majority of the included studies (n=39), the main task was to perform diagnosis of COVID-19 using lungs CT or X-Ray images. For these studies, GAN was This is a pre-print of the paper accepted for publication in JMIR Medical Informatics. is a GAN architecture that comprises two generators and two discriminators and does not require pair-to-pair training data [11] . Hence, it was a popular choice to generate COVID-19 positive images from normal images.

This review analyzed the common imaging modality for the different applications related to COVID-19. As chest X-Ray imaging and CT scans are the most popular imaging methods for studying the infection in individuals, the studies included in this review also used these two imaging modalities. Specifically, 35 studies used X-This is a pre-print of the paper accepted for publication in JMIR Medical Informatics. Final version will be available from JMIR. doi: 10.2196/37365

Ray images, and 21 studies used CT images. Some of the studies (n=6) also used both the CT and X-Ray images for diagnosis by training different models or for the transformation of images from X-Ray to CT. Though ultrasound imaging is not prevalent in the clinical diagnosis of COVID-19, one study reported using ultrasound images to diagnose COVID-19 with GANs. No other modality of imaging was used by the included studies.

The majority of the included studies (n=47) used data that is available publicly on Github, Kaggle, or other publicly accessible websites. These data are acquired from multiple sources (for example, collected from more than one hospital or through crowdsourcing) which makes them more diverse and hence more useful for training of GANs models. Similarly, it is hoped that the use of publicly accessible data will also encourage other researchers to conduct experiments on the data sets. The rise of publications in 2021 can also be linked to the availability of publicly available data sets that continued to rise as the number of COVID-19 infected cases continued to grow. A few of the included studies (n=10) used private or proprietary data sets, and hence, the details about those data sets are only limited to what has been described in the corresponding studies.

Only 13 studies provided information on the number of individuals whose data was used in the included studies. Amongst these, only one study [26] used data for more than 1000 individuals, and two studies [29] , [42] used data for more than 500

individuals. The remaining ten studies used data for less than 500 individuals. Given the size of the population infected with the COVID-19 (418+ million as of writing This is a pre-print of the paper accepted for publication in JMIR Medical Informatics. Final version will be available from JMIR. doi: 10.2196/37365 this, reported from John Hopkins University Coronavirus Resource Center 1 ), the need for experiments with much more extensive data is obvious. As a result of having more data, learning inherent features within the radiology images by using GANs will become more generalized with training on larger data. There is still more need to contribute to publicly accessible data. Similarly, researchers can also add to the existing dataset on Github by uploading their data to the current data repositories. An example of crowdsourcing of data is the COVIDx images repository for lungs X-ray images (see Table 3 ).

This review identified that the code to reproduce the results was not available for the majority of the studies. Only seven of the included studies provided a public link to the code. Availability of a public repository to reproduce the results for diagnosis or augmented data can help in advancing the research as well as increase the trust This is a pre-print of the paper accepted for publication in JMIR Medical Informatics. Final version will be available from JMIR. doi: 10.2196/37365 and reliance on the reported results in terms of the quality of the generated images or the accuracy reports for the diagnosis. Besides, the reproducibility by this code is not assessed by this review as it was beyond the scope of this review. Careful and responsible studies are needed to make an assessment of the published methods for transformation into clinical applications.

The majority of the included studies (n=43) did not provide information on the number of patients, although they did mention the number of images used in the experiments. So, it is unclear that how many images were used per individual.

Hence, the lack of information limits the ability of the readers to evaluate the performance in the context of the number of patients. Moreover, for public data set with crowd-sourced contributions, it is challenging to trace back the number of images to the number of individuals.

Validation of the performance of GANs in terms of the quality/usability of the generated images has a significant role in promoting the acceptability of the methods. In the included studies, only two studies reported that the results were presented to radiologists/clinicians for a secondary validation. For one study on the synthesis of X-Ray images, the radiologists agreed that the quality of the X-Rays has improved but falls short of diagnostic quality for use in clinics [45] . While using GANs methods in COVID-19 is tempting for many researchers, the lack of evaluation by radiologists or using GANs based methods without radiologists and clinicians in the loop will hinder the acceptability of these methods for clinical applications.

Besides, it is beyond the scope of this review to evaluate a study based on reporting of secondary evaluation by the radiologists, though a secondary assessment by the This is a pre-print of the paper accepted for publication in JMIR Medical Informatics. Final version will be available from JMIR. doi: 10.2196/37365

radiologists would have added value to the studies and increased their acceptability.

The lack of details related to the individuals whose COVID-19 data were used in these studies may also hinder their acceptance for transformation into clinical applications. The training of GANs is usually computationally demanding, requiring

GPUs. More edge computing-based implementations are needed for clinical applications to make these models compatible for implementation on low-power devices. This will increase the acceptability of these methods in clinical devices.

Though several reviews can be found on the applications of AI techniques in COVID-19, no review was found that focused on the potential of GANs based methods to combat COVID-19. Compared to other reviews [3, 4] , [6, 7] where the scope is too broad as they attempted to cover many different AI models, this review provides a comprehensive analysis of the GANs based approaches used primarily on lungs CT and X-Ray images. Similarly, many reviews cover the applications of GANs in medical imaging [10] , [12] [13] [14] [15] ; their applications in lungs images for COVID-19 have not been reviewed before. So, this review may be considered the first comprehensive review that covers all the GANs methods used for COVID-19 imaging data for different applications in general and data augmentation in particular. Thus, it is helpful for the readers to understand how GANs based approaches were used to address the problem of data scarcity and how the synthetic data (generated by GANs) was used to improve the performance of CNNs for COVID-19. This review This is a pre-print of the paper accepted for publication in JMIR Medical Informatics. Final version will be available from JMIR. doi: 10.2196/37365 provided a thorough list of the various publicly available datasets of lungs CT, lungs X-Ray, and lungs ultrasound images, along with the public URL. Hence, this can serve as a single point of contact for the readers to explore these data set resources and use them in their research work. This review is consistent with the guidelines of PRISMA-ScR for scientific reviews [16] .

This review included studies from five databases: Pubmed, IEEEXplore, ACM Digital Notably, the transformation of GANs based methods into clinical applications is still limited due to the limitations in the validation of the results, the generalization of the results, the lack of feedback from radiologists, and the limited explainability offered by these methods. Nevertheless, GANs based methods can assist in the performance enhancement of COVID-19 diagnosis even though they should not be used as independent tools. Besides, more research and advancements are needed towards the explainability and clinical transformations of these methods. This will pave the way for a broader acceptance of GANs based methods in COVID-19

applications.

This is a pre-print of the paper accepted for publication in JMIR Medical Informatics. Final version will be available from JMIR. doi: 10.2196/37365

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Deep Learning Models Using Auxiliary Classifier GAN for Covid-19 Detection-A Comparative Study

Portable Chest X-ray Synthetic Image Generation for the COVID-19

Segmentation of lungs in chest X-ray image using generative adversarial networks

Transfer learning and semi supervised adversarial detection and classification of This is a pre-print of the paper accepted for publication in JMIR Medical Informatics. Final version will be available from JMIR

COVID-19 in CT images

COVID-19 Automatic Detection Using Deep Learning

COVID-CGAN: Efficient Deep Learning Approach for COVID-19 Detection Based on CXR Images Using Conditional GANs. Applied Sciences

An Intelligent Parallel Distributed Streaming Framework for near Real-time Science Sensors and High-Resolution Medical Images

Signal propagation in a gradient-based and evolutionary learning system

Improving effectiveness of different deep learningbased models for detecting COVID-19 from computed tomography (CT) images

Developing an efficient deep neural network for automatic detection of COVID-19 using chest X-ray images

A preliminary analysis of AI based smartphone application for diagnosis of COVID-19 using chest X-ray images. Expert Systems with Applications

CT scan synthesis for promoting computer-aided diagnosis capacity of COVID-19

Covid-19 screening using a lightweight convolutional neural network with generative adversarial network data augmentation. Symmetry

Automated COVID-19 detection based on single-image super-resolution and CNN models. Computers, Materials and Continua

Adversarial neural network classifiers for COVID-19 diagnosis in ultrasound images. Computers, Materials and Continua

This is a pre-print of the paper accepted for publication in JMIR Medical Informatics. Final version will be available from JMIR

COVID-19 detection and disease progression visualization: Deep learning on chest X-rays for classification and coarse localization. Applied Intelligence

COViT-GAN: Vision Transformer for COVID-19 Detection in CT Scan Images with Self-Attention GAN for Data Augmentation

An H 2 O's Deep Learning-Inspired Model Based on Big Data Analytics for Coronavirus Disease (COVID-19) Diagnosis

Parallel/Distributed Generative Adversarial Neural Networks for Data Augmentation of COVID-19 Training Images

Improved CycleGAN with application to COVID-19 classification