Testing is one of the important methodologies used by various countries in order to fight against COVID-19 infection. The infection is considered as one of the deadliest ones although the mortality rate is not very high. COVID-19 infection is being caused by SARS-CoV2 which is termed as severe acute respiratory syndrome coronavirus 2 virus. To prevent the community, transfer among the masses, testing plays an important role. Efficient and quicker testing techniques helps in identification of infected person which makes it easier for to isolate the patient. Deep learning methods have proved their presence and effectiveness in medical image analysis and in the identification of some of the diseases like pneumonia. Authors have been proposed a deep learning mechanism and system to identify the COVID-19 infected patient on analyzing the X-ray images. Symptoms in the COVID-19 infection is well similar to the symptoms occurring in the influenza and pneumonia. The proposed model Inception Nasnet (INASNET) is being able to separate out and classify the X-ray images in the corresponding normal, COVID-19 infected or pneumonia infected classes. This testing method will be a boom for the doctors and for the state as it is a way cheaper method as compared to the other testing kits used by the healthcare workers for the diagnosis of the disease. Continuous analysis by convolutional neural network and regular evaluation will result in better accuracy and helps in eliminating the false-negative results. INASNET is based on the combined platform of InceptionNet and Neural network architecture search which will result in having higher and faster predictions. Regular testing, faster results, economically viable testing using X-ray images will help the front line workers to make a win over COVID-19.
Fuente: ISA Transactions
Available online 3 March 2022