Coronavirus detection and classification using x-rays and ct scans with machine learning techniques

Mohammed, M.; Srinivas, P. V. V. S.; Gowtham, V. P. S.; Raghavendra, A. V. K.; Lahari, G. K.

This work aims to detect the signs of the Coronavirus, also known as Covid-19. A dry cough, sore throat, and fever are the most common symptoms of Covid-19. For Covid-19, it is important to find fast and precise results at the time to stop it in the early stages and to avoid it from the vast spread. To interpret and identify the symptoms from X-rays and CT scan images, the machine learning and computer vision principles were applied. The works are usually performed with the CSV datasets. However, the analysis is performed to compare the images of patients with Covid and Non-Covid. To enhance the classification performance, it is feasible to use feature extraction techniques such as CNN, local directional pattern (LDP), gray-level run length matrix (GLRLM), gray-level scale zone matrix (GLSZM), and discrete wavelet transform (DWT) algorithms (Barstugan in Coro-navirus (Covid-19) classification using CT images by machine learning methods [1]). In this paper, the convolution neural network model is selected as the classi-fier. Softmax is used during the classification process to classify the images given, whether they belong to Covid or Non-Covid. This implementation is carried out on both the X-ray images dataset and the dataset of CT scan images which are obtained from the repository that is publicly accessible. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.