High-Performance Lung Disease Identification and Explanation Using a ReciproCAM-Enhanced Lightweight Convolutional Neural Network

Lung diseases significantly contribute to global morbidity and mortality, underscoring the importance of accurate and efficient diagnostic tools. This study introduces a lightweight two-dimensional convolutional neural network (LW-2DCNN) integrated with ReciproCAM, an explainable artificial intelligence (XAI) technique, specifically designed to classify multiple pulmonary conditions from chest computed tomography (CT) scans. Our model effectively addresses limitations of existing diagnostic methods by enhancing computational efficiency, diagnostic accuracy, and interpretability. Utilizing a curated dataset comprising 10,000 CT images from a Korean population, the LW-2DCNN achieved exceptional accuracy of 99.80% across five diagnostic categories: normal, lung cancer, pneumothorax, pneumonia, and interstitial lung disease (ILD). Comparative analyses demonstrated superior performance relative to established benchmark models, including EfficientNetB0, ResNet50, InceptionV3, DenseNet121, NASNetMobile, MobileNetV2, and Xception. The integrated ReciproCAM technique significantly enhanced clinical interpretability by highlighting the precise anatomical regions influencing the model’s diagnostic decisions. Ablation studies validated the optimal choice of the Adam optimizer and demonstrated the model’s computational feasibility, predicting and explaining 1000 images in approximately 150 seconds on a CPU-only device. This indicates strong potential for deployment in resource-constrained clinical settings.