Deep Convolutional Neural Networks for Detecting COVID-19 Using Medical Images: A Survey

Khattab, Rana; Abdelmaksoud, Islam R.; Abdelrazek, Samir

doi:10.1007/s00354-023-00213-6

Cited by 8 publications

(2 citation statements)

References 137 publications

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“…In [26], the authors surveyed the detection of COVID-19 using deep learning techniques, providing insights into the different approaches employed and evaluating their cost-effectiveness. Furthermore, [27] presents a survey focusing on deep convolutional neural networks (CNNs) for detecting COVID-19 using medical images. Its review highlights the state-of-the-art deep learning models for COVID-19 detection through medical imaging, discussing their strengths, limitations, and potential applications in clinical practice.…”

Section: Background and Related Workmentioning

confidence: 99%

Neural Networks for the Detection of COVID-19 and Other Diseases: Prospects and Challenges

et al. 2023

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Artificial neural networks (ANNs) ability to learn, correct errors, and transform a large amount of raw data into beneficial medical decisions for treatment and care has increased in popularity for enhanced patient safety and quality of care. Therefore, this paper reviews the critical role of ANNs in providing valuable insights for patients’ healthcare decisions and efficient disease diagnosis. We study different types of ANNs in the existing literature that advance ANNs’ adaptation for complex applications. Specifically, we investigate ANNs’ advances for predicting viral, cancer, skin, and COVID-19 diseases. Furthermore, we propose a deep convolutional neural network (CNN) model called ConXNet, based on chest radiography images, to improve the detection accuracy of COVID-19 disease. ConXNet is trained and tested using a chest radiography image dataset obtained from Kaggle, achieving more than 97% accuracy and 98% precision, which is better than other existing state-of-the-art models, such as DeTraC, U-Net, COVID MTNet, and COVID-Net, having 93.1%, 94.10%, 84.76%, and 90% accuracy and 94%, 95%, 85%, and 92% precision, respectively. The results show that the ConXNet model performed significantly well for a relatively large dataset compared with the aforementioned models. Moreover, the ConXNet model reduces the time complexity by using dropout layers and batch normalization techniques. Finally, we highlight future research directions and challenges, such as the complexity of the algorithms, insufficient available data, privacy and security, and integration of biosensing with ANNs. These research directions require considerable attention for improving the scope of ANNs for medical diagnostic and treatment applications.

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Section: Background and Related Workmentioning

confidence: 99%

Neural Networks for the Detection of COVID-19 and Other Diseases: Prospects and Challenges

et al. 2023

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“…Many different methods such as swab sample and radiology images have been used in the detection of COVID-19 [7][8][9]. In their study, Kanumuri and Chodavarapu developed a Convolutional Neural Networks (CNN) model for Automatic Diagnosis of COVID-19 Using Radiographic Images [10].…”

Section: Introductionmentioning

confidence: 99%

Neural Predictor Design for Covid-19 Cases in Different Regions

2023

IJOMAM

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, which emerged in the past years, has affected human life in many different ways. The COVID-19 virus has spread very quickly around the world and has become a pandemic. In many applications, artificial neural networks are used to estimate system parameters in realtime or simulation-based methods. In this study, the daily and total number of cases in Turkey, Italy and India are predicted. Three alternative areas, with or without following rules, are chosen for the COVID-19 cases. For this prediction process, 3 different neural network methods are used: Nonlinear autoregressive neural network (NAR-NN), Adaptive-Network Based Fuzzy Inference Systems (ANFIS) and Autoregressive integrated moving average (ARIMA). The results obtained for 3 different neural networks are given with graphs and tables. The conclusion of this study may be used to improve the precaution for the pandemic.

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Automatic Quantification of COVID-19 Pulmonary Edema by Self-supervised Contrastive Learning

Liang,

Xue,

Rajaraman

et al. 2023

Lecture Notes in Computer Science

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Deep Convolutional Neural Networks for Detecting COVID-19 Using Medical Images: A Survey

Cited by 8 publications

References 137 publications

Neural Networks for the Detection of COVID-19 and Other Diseases: Prospects and Challenges

Neural Networks for the Detection of COVID-19 and Other Diseases: Prospects and Challenges

Neural Predictor Design for Covid-19 Cases in Different Regions

Automatic Quantification of COVID-19 Pulmonary Edema by Self-supervised Contrastive Learning

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