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Reports, Volume 5, Issue 2
June 2022 - 13 articles
Cover Story: The aim of this study is to offer a deeper insight into the image features that deep learning extracts in COVID-19 detection tasks from X-ray technology. This study offers an analysis of the image features extracted from MobileNet (v2), aiming to investigate the validity of these features and their medical importance. The pipeline can detect abnormal X-rays with an accuracy of 95.45 ± 1.54% and distinguish COVID-19 with an accuracy of 89.88 ± 3.66%. The visualized results of the Grad-CAM algorithm provide evidence that the methodology identifies meaningful areas on the images. Finally, the detected image features were reproducible 98% of the time after repeating the experiment three times. View this paper
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Reports - ISSN 2571-841X