ChestX-Ray6: Prediction of multiple diseases including COVID-19 from chest X-ray images using convolutional neural network

Md Nahiduzzaman; Md Rabiul Islam; Rakibul Hassan

doi:10.1016/j.eswa.2022.118576

ChestX-Ray6: Prediction of multiple diseases including COVID-19 from chest X-ray images using convolutional neural network

Expert Syst Appl. 2023 Jan:211:118576. doi: 10.1016/j.eswa.2022.118576. Epub 2022 Aug 27.

Authors

Md Nahiduzzaman¹, Md Rabiul Islam², Rakibul Hassan¹

Affiliations

¹ Department of Electrical & Computer Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh.
² Department of Computer Science & Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh.

Abstract

In the last few decades, several epidemic diseases have been introduced. In some cases, doctors and medical physicians are facing difficulties in identifying these diseases correctly. A machine can perform some of these identification tasks more accurately than a human if it is trained correctly. With time, the number of medical data is increasing. A machine can analyze this medical data and extract knowledge from this data, which can help doctors and medical physicians. This study proposed a lightweight convolutional neural network (CNN) named ChestX-ray6 that automatically detects pneumonia, COVID19, cardiomegaly, lung opacity, and pleural from digital chest x-ray images. Here multiple databases have been combined, containing 9,514 chest x-ray images of normal and other five diseases. The lightweight ChestX-ray6 model achieved an accuracy of 80% for the detection of six diseases. The ChestX-ray6 model has been saved and used for binary classification of normal and pneumonia patients to reveal the model's generalization power. The pre-trained ChestX-ray6 model has achieved an accuracy and recall of 97.94% and 98% for binary classification, which outweighs the state-of-the-art (SOTA) models.

Keywords: COVID19; Cardiomegaly; ChestX-Ray6; Convolutional neural network (CNN); DenseNet121; Lung opacity; MobileNetV2; Pleural; Pneumonia; ResNet50; VGG19.