Covid-19 and Tuberculosis Classification Based on Chest X-Ray Using Convolutional Neural Network

Abstract

The high rate of patients with tuberculosis (TB) with the graph showing a continual increase requires the research in any sector as the programs to eradicate tuberculosis. One of the applications is the Decision Support System (DSS) that helps the medical experts particularly doctors in diagnosing TB grade 1+. 2+, and 3+ rapidly. Another problem is related to the imbalance between the number of patients and the number of medical practitioners in the condition of pandemic Corona Virus Disease (Covid-19) today. Hence, DSS is highly required and it can be used for the long-term management of Covid. In this study, the rapid classification of normal lung, tuberculosis lung, and Covid-19 lung based on the Chest X-Ray (CXR) image was proposed as the initial step of DSS implementation. The proposed image processing based CXR classification using Deep Learning Convolutional Neural Network (CNN) obtained the highest accuracy rate of 88.37%. This accuracy was obtained in the second scenario with the 208 CXR datasets. The small number of datasets used was related to the limited number of CXR Covid-19 images with good quality brightness. The proposed system developed is expected to help doctors in diagnose lung disease.