SARS Detection: Automated Detection Of COVID-19 Through Convolutional Neural Network Using Lungs CT-Scan

SARS i.e COVID-19 has become a global disaster that has disturbed the socioeconomic fabric of the world. Efficient and cost-effective diagnosis methods are very much required for better treatment and eliminating false cases for COVID-19. COVID-19 disease is a type of respiratory syndrome, thus lung CT-scan analysis has got the attention for an effective diagnosis. Hence, the proposed study introduces an Image processing based COVID-19 detection model , which is trained on a dataset of lungs CT-scan images belonging to three categories: COVID-19 and Normal person i.e non covid person. Image preprocessing pipeline is used for extracting the region of interest (ROI), so that the required features may be present in the input. This lightweight convolution neural network (CNN) based approach has achieved an accuracy of 96.39% . Model input images are the dataset from Kaggle . The performance has outperformed the state-of-the-art. The promising results will surely help in accelerating the development of deep learning-based COVID-19 diagnosis tools using radiography.