文章基本信息

标题：COVID-19 Cases Detection from Chest X-Ray Images using CNN based Deep Learning Model
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作者：Md Amirul Islam ; Giovanni Stea ; Sultan Mahmud 等
期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2022
卷号：13
期号：5
DOI：10.14569/IJACSA.2022.01305108
语种：English
出版社：Science and Information Society (SAI)
摘要：COVID-19 has recently manifested as one of the most serious life-threatening infections and is still circulating globally. COVID-19 can be contained to a considerable extent if a patient can know their COVID-19 infection at a possible earlier time, and they can be isolated from other individuals. Recently, researchers have explored AI (Artificial Intelligence) based technologies like deep learning and machine learning strategies to identify COVID-19 infection. Individuals can detect COVID-19 disease using their phones or computers, dispensing with the need for clinical specimens or visits to a diagnostic center. This can significantly reduce the risk of spreading COVID-19 farther from a probably infected patient. Motivated by the above, we propose a deep-learning model using CNN (Convolutional Neural Networks) to autonomously diagnose COVID-19 disease from CXR (Chest X-ray) images. The dataset used to train our model includes 10293 X-ray images, with 875 X-ray images from COVID-19 cases. The dataset contains three different classes of the tuple: COVID-19, pneumonia, and normal cases. The empirical outcomes show that the proposed model achieved 97%specificity, 96.3% accuracy, 96% precision, 96% sensitivity, and 96% F1-score, respectively, which are better than the available works, despite using a CNN with fewer layers than those.
关键词：COVID-19; CNN; deep learning; machine learning; chest X-ray