Advanced Lung Disease Detection: CBAM-Augmented, Lightweight EfficientNetB2 with Visual Insights.

Abstract

Introduction: This paper presents a multichannel deep-learning method for detecting lung diseases using chest X-ray images. Using EfficientNetB0 through EfficientNetB7 pretrained models, the methodology offers improved performance in classifying COVID-19, viral pneumonia, and normal chest Xrays.

Methods: The EfficientNetB2 model was customized by incorporating Squeeze-and-Excitation (SE) blocks and the Convolutional Block Attention Module (CBAM) to improve the model's attention mechanisms. Additional convolutional layers were added for improved feature extraction, and multiscale feature fusion was implemented to capture features at different scales.

Results: In this study, 99.3% of the unseen chest X-ray images were identified using the proposed model. It demonstrated superior performance, surpassing existing techniques and highlighting its robustness and generalizability on unseen data samples.

Conclusion: Moreover, visualization techniques were used to inspect the intermediate layers of the model, providing deeper insights into its processing and interpretation of medical images. The proposed method offers healthcare radiologists a valuable tool for rapid and accurate point-of-care diagnoses.