Lung-Related Diseases Classification Using Deep Convolutional Neural Network

Abstract

Accurate diagnosis is a crucial first step in the management and treatment of lung diseases, which include infectious diseases such as COVID-19, viral pneumonia, lung opacity, tuberculosis, and bacterial pneumonia. Despite these conditions sharing similar manifestations in chest X-ray images, it is imperative to correctly identify the disease present. This study, therefore, sought to develop a convolutional neural network (CNN)- based model for the classification of lung diseases. Four distinct CNN models, namely MobileNetV2, ResNet-50, ResNet-101, and AlexNet, were rigorously evaluated for their ability to classify lung diseases from chest X-ray images. These models were tested against three classification schemes to examine the impact of high interclass similarity: a 4-subclass classification (COVID-19, viral pneumonia, lung opacity, and normal), a 5-subclass classification (COVID-19, viral pneumonia, lung opacity, tuberculosis, and normal), and a 6-subclass classification (COVID-19, lung opacity, viral pneumonia, tuberculosis, bacterial pneumonia, and normal). The retrained ResNet-50 architecture yielded the best results, achieving a classification accuracy of 97.22%, 92.14%, and 96.08% for the 6-subclass, 5-subclass, and 4-subclass classifications respectively. Conversely, ResNet-101 demonstrated the lowest classification accuracy for the 6-subclass and 5-subclass classifications, with 78.12% and 79.49% respectively, while MobileNetV2 had the lowest accuracy for the 4-subclass classification, with 88.89%. These results suggest that, despite high interclass similarity, the ResNet-50 model can effectively classify lung-related diseases from chest X-ray images. This finding supports the use of computer-aided detection (CAD) systems as decision-support tools in the early classification of lung-related diseases.