Hybrid Ensemble Learning for Multi-Class Chest X-Ray Classification Using Deep CNN

Abstract

Background: COVID-19, pneumonia, and TB (tuberculosis) are still the big killers of people suffering from chest disease and continue to be a serious health challenge globally. A timely diagnosis leads to timely treatment and improved patient outcomes. Chest X-ray (CXR) imaging is widely used for diagnostic purposes due to its speed, low cost, and availability in most healthcare facilities. Manual reading of CXR images is, however, challenging because of the similarity in the presentation of radiographic features across various chest diseases. Methods: This research introduces a hybrid ensemble learning approach to classify chest X-ray images into four classes—Normal, COVID-19, Pneumonia, and Tuberculosis. Three Deep CNN network models, namely Xception, AlexNet, and EfficientNet-B0, were used for deep feature extraction. Additionally, texture features of the images were extracted using Gabor filters. The deep and texture features were combined and classified using logistic regression and a stacking ensemble learning approach. A publicly available chest X-ray image database containing 7,135 X-rays was used, with six-fold stratified cross-validation to assess the proposed approach. Results: The ensemble models outperformed the individual CNN models. The Average Ensemble produced the best results with an accuracy of 91.18%, an Average Precision (AP) of 96.75%, and an Area Under the ROC Curve (AUC) of 98.87%. The proposed model performs well across all four disease classes. It exhibited high sensitivity in detecting Tuberculosis with considerable stability in classifying Normal, COVID-19, and Pneumonia. Conclusion: The proposed framework demonstrates the effectiveness of integrating deep learning features, Gabor texture features, and ensemble learning for improved chest X-ray image classification. This can help computer-assisted diagnostics systems and aid medical workers in identifying chest diseases early.