Covid-19 Detection in X-Ray Images UsingDeepLearningAlgorithms (International Journal of Food and Nutritional Sciences, 2022)
TheCOVID-19 pandemic had a variety of effects on global health, the global economy, and global lifestyle. Thus, it is essential to identify viruses early in order to treat patients more effectively. With the help of deep learning methods such convolution neural networks (CNN), VGG16, and VGG19, thisresearchwillexamine the detectionof COVIDvirusinx-raypictures. The actual diagnosis test, called RT-PCR for reversetranscription polymerase chain reaction, is quite expensive andtakes a long time to get results. Thus, additional sophisticatedtesting and diagnostic instruments are required. Inspired by therecent research that is used to detect the COVID-19 presence inthe X-ray images, this research uses deep learning methods andalgorithms to evaluate these images and classify them as covid positive and covid negative cases respectively. The proposed approach includes the preprocessing of the x-ray images which includes removing of their relevant surroundings and bias producing results. After the preprocessing stage, training the classification model under the transfer learning scheme, and outputs are analysed and interpreted through visualization. In this approach, we achieved the accuracy of 95% using the CNN model.