X-Ray and CT-Scan-Based Automated Detection and Classification of Covid-19 Using Convolutional Neural Networks (CNN)

Thakur, Samritika; Kumar, Aman

Covid-19 (Coronavirus Disease-2019) is the most recent coronavirus-related disease that has been announced as a pandemic by the World Health Organization (WHO). Furthermore, it has brought the whole planet to a halt as a result of the worldwide introduction of lockdown and killed millions of people. While this virus has a low fatality rate, the problem is that it is highly infectious, and as a result, it has infected a large number of people, putting a strain on the healthcare system, hence, Covid-19 identification in patients has become critical. The goal of this research is to use X-rays images and computed tomography(CT) images to introduce a deep learning strategy based on the Convolutional Neural Network (CNN) to automatically detect and identify the Covid-19 disease. We have implemented two different classifications using CNN(binary classification and multiclass classification). A total of 3,877 image datasets from CTs and X-rays were utilised to train the model in binary classification, with 1,917 images from Covid-19 infected individuals among them. The experimental results for binary classification show an overall accuracy of 99.64%, recall(or sensitivity) of 99.58%, the precision of 99.56%, F1-score of 99.59%, and ROC of 100%. For multiple classifications, the model was trained using a total of 6,077 images, with 1,917 images of Covid-19 infected people, 1,960 images of normal healthy people, and 2,200 images of pneumonia infected people. The experimental results for multiple classifications show an accuracy of 98.28%, recall(or sensitivity) of 98.25%, the precision of 98.22%, F1-score of 98.23%, and ROC of 99.87%. On the currently available dataset, the model produced the desired results, and it can assist healthcare workers in quickly detecting Covid-19 positive patients.