COVID-19 Automatic Detection Using Deep Learning

Sanajalwe, Yousef; Anbar, Mohammed; Al-Emari, Salam

doi:10.32604/csse.2021.017191

Cited by 9 publications

(5 citation statements)

References 53 publications

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“…The majority of the studies used GANs to augment the data, where they reported the use of GANs to increase the data set size. Specifically, 42 (74%) studies used GAN-based methods for data augmentation [ 18 , 21 , 23 - 29 , 31 - 36 , 38 - 43 , 45 , 46 , 48 , 50 , 52 - 56 , 59 - 67 , 71 , 73 , 74 ]. The augmented data were then used to improve the training of different CNNs to diagnose COVID-19.…”

Section: Resultsmentioning

confidence: 99%

“…Specifically, the use of X-ray images dominated the studies. In total, 29 (51%) studies used X-ray images of lungs [ 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 ], while 21 (37%) studies used CT images [ 18 , 19 , 22 - 24 , 26 , 30 , 33 , 34 , 36 , 38 , 48 , 49 , 51 , 53 , 55 , 58 , 61 , 63 , 66 , 71 ], and 6 (11%) studies reported the use of both X-ray and CT images [ 39 , 44 , 46 , 47 , 68 , 72 ]. Only 1 (2%) study used ultrasound images for COVID-19 diagnosis [ 69 ], which shows that ultrasound is not a popular imaging modality for training GANs and other deep learning models for COVID-19 detection (also see Figure 4 ).…”

Section: Resultsmentioning

confidence: 99%

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Combating COVID-19 Using Generative Adversarial Networks and Artificial Intelligence for Medical Images: Scoping Review

Ali¹,

Shah²

2022

JMIR Med Inform

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Background Research on the diagnosis of COVID-19 using lung images is limited by the scarcity of imaging data. Generative adversarial networks (GANs) are popular for synthesis and data augmentation. GANs have been explored for data augmentation to enhance the performance of artificial intelligence (AI) methods for the diagnosis of COVID-19 within lung computed tomography (CT) and X-ray images. However, the role of GANs in overcoming data scarcity for COVID-19 is not well understood. Objective 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 different GAN methods and lung imaging data sets 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. Methods A search was conducted on 5 databases, namely PubMed, IEEEXplore, Association for Computing Machinery (ACM) Digital Library, Scopus, and Google Scholar. The search was conducted from October 11-13, 2021. The search was conducted using intervention keywords, such as “generative adversarial networks” and “GANs,” and application keywords, such as “COVID-19” and “coronavirus.” The review was performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines for systematic and scoping reviews. Only those studies were included that reported GAN-based methods for analyzing chest X-ray images, chest CT images, and chest ultrasound images. Any studies that used deep learning methods but did not use GANs were excluded. No restrictions were imposed on the country of publication, study design, or outcomes. Only those studies that were in English and were published from 2020 to 2022 were included. No studies before 2020 were included. Results This review included 57 full-text studies that reported the use of GANs for different applications in COVID-19 lung imaging data. Most of the studies (n=42, 74%) 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 superresolution of lung images. The cycleGAN and the conditional GAN were the most commonly used architectures, used in 9 studies each. In addition, 29 (51%) studies used chest X-ray images, while 21 (37%) studies used CT images for the training of GANs. For the majority of the studies (n=47, 82%), the experiments were conducted and results were reported using publicly available data. A secondary evaluation of the results by radiologists/clinicians was reported by only 2 (4%) studies. Conclusions Studies have shown that GANs have great potential to address the data scarcity challenge for lung images in COVID-19. Data synthesized with GANs have been helpful to i...

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Combating COVID-19 Using Generative Adversarial Networks and Artificial Intelligence for Medical Images: Scoping Review

Ali¹,

Shah²

2022

JMIR Med Inform

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“…Our model can quickly detect wear or not wearing a mask by producing a higher accuracy with tiny loss. Based on the experimental result, the proposed model can be a promising solution to deal with face mask detection issues using the real and huge dataset [19][20][21][22][23][24][25][26][27].…”

Section: Discussionmentioning

confidence: 99%

DeepMask: face mask detection using GAN algorithm

2022

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COVID-19 pandemic is the main reason people must wear face masks in public places. Traditionally, officers monitor the use of face masks in the public area manually. However, monitoring masks using manual techniques is challenging in a crowded spot. Thus, we propose a face mask detection based on Generative Adversarial Networks (GAN) through the learning model to accelerate mask detection accurately and quickly. To construct our detection model, we collect the dataset, conduct pre-processing, and train the model by tuning multiple parameters to obtain the highest accuracy and tiny loss. The experimental results can produce D_Loss = 0.0032 and G_Loss = 7.3296. Therefore, the proposed model can be a promising solution for mask detection issues.

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“…The equations utilized to assess the performance of feature selection are illustrated in Table 5. The symbols used in the equations are as follows: TP represents the count of true positives, FN represents the count of false negatives, TN represents the count of true negatives, and FP represents the count of false positives [50], [51], [52], [53], [54], [55].…”

Section: A Evaluation Metricsmentioning

confidence: 99%

Abnormal Transactions Detection in the Ethereum Network Using Semi-Supervised Generative Adversarial Networks

Sanjalawe,

Al-E’mari

2023

IEEE Access

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Numerous abnormal transactions have been exposed as a result of targeted attacks on Ethereum, such as the Ethereum Decentralized Autonomous Organization attack. Exploiting vulnerabilities in smart contracts, malicious users can pursue their own illicit objectives through abnormal transactions. Consequently, identifying these malevolent users, implicated in fraudulent activities and their attribution, becomes exceedingly complex. Cryptocurrency transactions used for malicious purposes, employing pseudo-anonymous accounts to send and receive ransom payments and accumulating funds under various identities, further highlight the need to control and detect these abnormal transactions for maintaining a high level of security within the Ethereum network. Although existing Intrusion Detection Systems (IDSs) help mitigate abnormal transaction occurrences, their performance necessitates improvement. To address this issue, this study presents a novel approach, named Abnormal Transactions Detection Using a Semi-Supervised Generative Adversarial Network (ATD-SGAN), which efficiently detects abnormal attacks within the Ethereum network. ATD-SGAN leverages a semi-supervised generative adversarial network for this purpose. The results demonstrate that ATD-SGAN significantly enhances the performance of state-ofthe-art IDSs. It achieves an increase in detection accuracy from 3.78% to 11.05% and reduces the false alarm rate from 42.29% to 0.15%. Moreover, ATD-SGAN notably improves the F1-measure, ranging from 10.39% to 3.79%, compared to the current IDSs.

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COVID-19 Automatic Detection Using Deep Learning

Cited by 9 publications

References 53 publications

Combating COVID-19 Using Generative Adversarial Networks and Artificial Intelligence for Medical Images: Scoping Review

Combating COVID-19 Using Generative Adversarial Networks and Artificial Intelligence for Medical Images: Scoping Review

DeepMask: face mask detection using GAN algorithm

Abnormal Transactions Detection in the Ethereum Network Using Semi-Supervised Generative Adversarial Networks

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