Lung Opacity Classification With Convolutional Neural Networks Using Chest X-rays

Chest X-ray interpretation is very crucial to detect cardiothoracic and pulmonary abnormalities. This time-consuming and tedious task should be error-free, fast, and reliable otherwise a single mistake may cause serious harm to patients. Recently, deep learning achieves radiologist-level performance in chest X-ray interpretation. A CAD (Computer-aided detection system) can help radiologists to review chest X-rays fast and accurately. In our research, we trained and evaluated various deep convolutional neural networks (CNN) architectures to detect potential lung opacity from chest X-rays. We observed how strongly architectures could differentiate lung opacity from normal and other abnormal chest X-rays. In these circumstances, A CNN based model (Xception) achieved 91.0% AUC along with 83.95% accuracy. Moreover, we also observed models achieved a better performance on lung opacity vs normal chest X-ray classification (excluding abnormal class) where Xception achieved 99.1% AUC, 97.19% sensitivity, and 95.71%accuracy. Therefore, the purpose of this study is to investigate the classification ability of deep CNN architectures which helps to develop an automatic lung opacity detection system.