Enhancing an Imbalanced Lung Disease X-ray Image Classification with the CNN-LSTM Model

Abstract

Lung diseases have a significant impact on respiratory health, causing various symptoms and posing challenges in diagnosis and treatment. This research presents a methodology for classifying lung diseases using chest X-ray images, specifically focusing on COVID-19, pneumonia, and normal cases. The study introduces an optimal architecture for convolutional neural network (CNN) and long short-term memory (LSTM) models, considering evaluation metrics and training efficiency. Furthermore, the issue of imbalanced datasets is addressed through the application of some image augmentation techniques to enhance model performance. The most effective model comprises five convolutional blocks, two LSTM layers, and no augmentation, achieving an impressive F1 score of 0.9887 with a training duration of 91 s per epoch. Misclassifications primarily occurred in normal cases, accounting for only 3.05% of COVID-19 data. The pneumonia class demonstrated excellent precision, while the normal class exhibited high recall and an F1 score. Comparatively, the CNN-LSTM model outperformed the CNN model in accurately classifying chest X-ray images and identifying infected lungs. This research provides valuable insights for improving lung disease diagnosis, enabling timely and accurate identification of lung diseases, and ultimately enhancing patients’ outcomes.