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Ahmed, Mohammed, Fakhrudeen, Ahmed. (1401). Deep learning-based COVID-19 detection: State-of-the-art in research. هنرهای کاربردی, 14(1), 1939-1962. doi: 10.22075/ijnaa.2022.7119
Mohammed Saleh Ahmed; Ahmed M. Fakhrudeen. "Deep learning-based COVID-19 detection: State-of-the-art in research". هنرهای کاربردی, 14, 1, 1401, 1939-1962. doi: 10.22075/ijnaa.2022.7119
Ahmed, Mohammed, Fakhrudeen, Ahmed. (1401). 'Deep learning-based COVID-19 detection: State-of-the-art in research', هنرهای کاربردی, 14(1), pp. 1939-1962. doi: 10.22075/ijnaa.2022.7119
Ahmed, Mohammed, Fakhrudeen, Ahmed. Deep learning-based COVID-19 detection: State-of-the-art in research. هنرهای کاربردی, 1401; 14(1): 1939-1962. doi: 10.22075/ijnaa.2022.7119

Deep learning-based COVID-19 detection: State-of-the-art in research

International Journal of Nonlinear Analysis and Applications
مقاله 152، دوره 14، شماره 1، فروردین 2023، صفحه 1939-1962    اصل مقاله (2.28 M)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22075/ijnaa.2022.7119
نویسندگان
Mohammed Saleh Ahmed* 1؛ Ahmed M. Fakhrudeen2
1Computer Science Department, College of Computer Science and Information Technology, Kirkuk University, Kirkuk, Iraq
2Software Department, College of Computer Science and Information Technology, Kirkuk University, Kirkuk, Iraq
تاریخ دریافت: 14 مرداد 1401،  تاریخ بازنگری: 28 شهریور 1401،  تاریخ پذیرش: 24 مهر 1401 
چکیده
In the last two years, the coronavirus (COVID-19) pandemic put healthcare systems around the world under tremendous pressure. Imaging techniques (like Chest X-rays) play an essential role in diagnosing many diseases (such as COVID-19). There have been intelligent systems (Machine Learning (ML) and Deep Learning (DL)) able to identify COVID-19 from similar normal diseases. In this paper, we start by overviewing the status of COVID-19 from a historical standpoint and diagnosis updates. Moving on, provide an overview of the convolutional neural networks. Then, we elaborate Transfer learning method and its main approaches. Next, we provide a critical literature review on implementing Deep learning techniques: 1) Novel deep learning architecture; 2) Direct use of deep learning; 3) Transfer learning fine-tuning technique, and 4) Transfer learning feature extraction technique. For each of these, we evaluate and compare very recent studies published in highly ranked journals. The experiments show that all techniques achieve closer accuracy, ranging from (98-100 \%). Along with all, the direct use of the deep learning technique records the highest accuracy and is less time-consuming and resource spending. Therefore, establishing such a technique is useful to predict the outbreak early, which in turn can aid in controlling the pandemic effectively.
کلیدواژه‌ها
COVID-19؛ Deep learning؛ Machine learning؛ X-rays
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