Online COVID-19 Infection Diagnoses via Chest X-Ray Images using Alexnet Deep Learning Model - Ali Heidari; Hamidreza Erfanian (International Journal of Web Research)
Since the outbreak of Covid19 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 Covid19 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 10,000 chest images, half of which are related to CXR images and the other half are related to patients with Covid19 infection. The model proved to be very reliable with 99.26% accuracy in diagnosis and 95% sensitivity and 99.7% specificity.