Online COVID-۱۹ Infection Diagnoses via Chest X-Ray Images using Alexnet Deep Learning Model
Publish place: International Journal of Web Research، Vol: 5، Issue: 1
Publish Year: 1401
نوع سند: مقاله ژورنالی
زبان: English
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شناسه ملی سند علمی:
JR_IJWR-5-1_007
تاریخ نمایه سازی: 5 شهریور 1401
Abstract:
Since the outbreak of Covid۱۹ virus to date, various methods have been introduced in order to diagnose the virus infection. One of the most reliable tests is assessing frontal Chest X-Ray(CXR) images. As the virus causes inflammation in the infected patient's lung, it is possible to diagnose whether one is infected or not using his/her CXR image. in contrast to other tests which mostly are based on the virus genome, this test is not time-consuming and it is reliable against new strains of the virus. However, this test requires a specialist to assess the CXR images. As the datasets of Covid۱۹ patient CXR images are increasing in number, it is possible to use machine learning techniques in order to assess CXR images automatically and even online. In this study, we used deep learning approaches and we fine-tuned the Alexent in order to automatically classify CXR images and label the whether "Covid" or "Normal". The data we used in this study include about ۱۰,۰۰۰ chest images, half of which are related to CXR images and the other half are related to patients with Covid۱۹ infection. The model proved to be very reliable with ۹۹.۲۶% accuracy in diagnosis and ۹۵% sensitivity and ۹۹.۷% specificity.
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Authors
Ali Heidari
Department of Bioinformatic, University of Science and Culture, Tehran, Iran
Hamidreza Erfanian
Department of Bioinformatics,University of Science and Culture, Tehran,Iran