Classification of Lung Opacity, COVID-19, and Pneumonia from Chest Radiography Images Based on Convolutional Neural Networks

Abstract

Detection of normal and abnormal lung images from the human chest through media scans in the form of computed tomography (CT) scans or radiographs has received attention for early diagnosis of patients. However, even in the study, the diagnosis obtained better accuracy from CT scan results to detect lungs infected with coronavirus disease 2019 (COVID-19) than a swab test with the polymerase chain reaction (PCR) method. This paper aims to detect and classify normal lungs, lung opacities, lungs infected with COVID-19, and viral pneumonia (in this paper is more commonly written as pneumonia) from human chest radiography images. This paper uses 5000 image data consisting of 2461 typical lung images, 1347 lung opacity images, 295 pneumonia images, and 897 COVID-19 images. The method used to detect and classify the labeled images uses the convolutional neural networks (CNN) method. Several image detection and classification studies often implement this method. Comparing the image data for training and testing data uses a ratio of 80 and 20, respectively. Accuracy results for data training got 99.825%, while data testing got 82.6%.