(Retracted) COVID-19 detection using machine learning: a large scale assessment of x-ray and CT image datasets

Sohan, Fahimuzzman; Basalamah, Anas; Solaiman, Md.

doi:10.1117/1.jei.31.4.041212

Cited by 8 publications

(9 citation statements)

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“…We elaborately discussed from every perspective, including demographic data, privacy appearance, datasets, FL characteristics, model implementation, and performance comparison. We noticed in one of our previous articles [33] that deep learning oriented COVID-19 detection using X-ray and CT images has high accuracy, most of them achieved more than 95% accuracy. We further observed a similar trend in this study, here COVID-19 detection research articles are the top scorers with FL mechanism.…”

Section: Discussionmentioning

confidence: 91%

“…The RT-PCR test is the most reliable diagnosis method of the diseases, since inadequate testing kits and some technical limitations, researchers tried to explore alternative ways of COVID screening. Therefore, hundreds of ML based automated and time saving COVID-19 detection models have been presented within the last two years [33]. ML based COVID analysis is mostly carried out by radiological chest images, i.e., X-ray and CT images.…”

Section: A Overviewmentioning

confidence: 99%

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A Systematic Review on Federated Learning in Medical Image Analysis

Sohan

Basalamah

2023

IEEE Access

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Federated Learning (FL) obtained a lot of attention to the academic and industrial stakeholders from the beginning of its invention. The eye-catching feature of FL is handling data in a decentralized manner which creates a privacy preserving environment in Artificial Intelligence (AI) applications. As we know medical data includes marginal private information of patients which demands excessive data protection from disclosure to unexpected destinations. In this paper, we performed a Systematic Literature Review (SLR) of published research articles on FL based medical image analysis. Firstly, we have collected articles from different databases followed by PRISMA guidelines, then synthesized data from the selected articles, and finally we provided a comprehensive overview on the topic. In order to do that we extracted core information associated with the implementation of FL in medical imaging from the articles. In our findings we briefly presented characteristics of federated data and models, performance achieved by the models and exclusively results comparison with traditional ML models. In addition, we discussed the open issues and challenges of implementing FL and mentioned our recommendations for future direction of this particular research field. We believe this SLR has successfully summarized the state-of-the-art FL methods for medical image analysis using deep learning.INDEX TERMS Federated learning, machine learning, medical image analysis, data privacy, systematic literature review.

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Section: Discussionmentioning

confidence: 91%

Section: A Overviewmentioning

confidence: 99%

A Systematic Review on Federated Learning in Medical Image Analysis

Sohan

Basalamah

2023

IEEE Access

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“…Such models recognize diseases with a high accuracy level. This same approach can be further generalized and applied to most multiclass classification problems 29 – 31 However, a limitation of this study is the slow execution time since models based on CNNs with a larger number of layers converge slowly.…”

Section: Discussionmentioning

confidence: 99%

Retinal disease classification based on optical coherence tomography images using convolutional neural networks

2022

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The challenges in today's medicine are progressively more related to the application of artificial intelligence and supervised learning techniques. Optical coherence tomography (OCT) is a noninvasive imaging technology used to obtain high-resolution cross-sectional images of the retina. The layers within the retina can be differentiated and retinal thickness can be measured to facilitate early detection and diagnosis of retinal diseases and conditions. This research paper is aimed at exploring different possibilities of applying deep learning, specifically convolutional neural networks in retinal diseases. During the research, several different architectures-AlexNet, VGG, Inception, and residual network, were evaluated, and the convolutional neural network that proved to be the most successful in the classification was based on the Inception architecture. Hyperparameter tuning was applied as the main method to find the most optimal solution. The key contributions of this research refer to the analysis of different architectures that can be applied in the classification of retinal diseases based on OCT images, as well as the evaluation of the test set obtained by comparing different models with different hyperparameters. This research yielded the best results obtained with Inception1, when training by means of the root mean square propagation optimizer with a batch size of 32, learning rate of 1e −6 , momentum of 0.99, and L2 regularization rate of 0.001. This model achieved an accuracy of 0.95528. In the conclusion of the paper, the advantages of the proposed and implemented solutions were discussed, and a proposal for further improvements was proposed.

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“…4,5 Nevertheless, due to the limitation of chest CT datasets, most of them cannot generate great results for clinical auxiliary detection. 6 Transfer learning has been proved to be an effective way to solve these problems and always generates higher accuracy and better training efficiency. 7,8 These transfer learning-based techniques can be divided into three categories: 9 (1) fully adaptive techniques, (2) partially adaptive techniques, and (3) zero adaptive techniques.…”

Section: Introductionmentioning

confidence: 99%

“…Multiple scholars have conducted effective experiments and published several COVID-19 detection related papers 4 , 5 . Nevertheless, due to the limitation of chest CT datasets, most of them cannot generate great results for clinical auxiliary detection 6 . Transfer learning has been proved to be an effective way to solve these problems and always generates higher accuracy and better training efficiency 7 , 8 …”

Section: Introductionmentioning

confidence: 99%

Targeted self-supervised attention network for coronavirus disease 2019 detection

Ding

2023

J. Electron. Imag.

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.The worldwide spread of the coronavirus disease 2019 (COVID-19) has had a significant impact on healthcare systems globally. Timely and accurate diagnosis is crucial for controlling and preventing outbreaks. Computed tomography (CT) has been demonstrated as an effective diagnostic tool for COVID-19. Deep learning techniques have been applied in related fields to solve relevant problems. Normally, they achieve good performance on large datasets directly. However, due to the need to protect patient privacy and the sudden outbreak of the epidemic, the amount of publicly available CT data is insufficient for utilizing these methods. In addition, some natural images, which are used for model pre-training, are quite different from chest CT images, causing some inevitable errors. To address these issues, a targeted self-supervised attention network based on an improved inception ResNet called SIRANet is proposed. SIRANet incorporates a self-supervised learning method during the pre-training process and introduces a attention module to improve feature selection and focus on key informative features. Moreover, a transfer learning-based strategy for COVID-19 detection is presented and experiments are conducted on three widely used datasets, including ImageNet, an unlabeled chest CT dataset, and a COVID-19 CT dataset. Experimental results show that SIRANet outperforms other state-of-the-art techniques in terms of accuracy, precision, recall, and f1-score, demonstrating its effectiveness in capturing key informative features from unlabeled and labeled images. The implementation of SIRANet is available at https://github.com/Xiiin6/COVID-19.

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(Retracted) COVID-19 detection using machine learning: a large scale assessment of x-ray and CT image datasets

Cited by 8 publications

References 0 publications

A Systematic Review on Federated Learning in Medical Image Analysis

A Systematic Review on Federated Learning in Medical Image Analysis

Retinal disease classification based on optical coherence tomography images using convolutional neural networks

Targeted self-supervised attention network for coronavirus disease 2019 detection

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