An Interpretable Deep Learning Framework for Multi-Class Lung Disease Diagnosis Using ConvNeXt Architecture - Basyir, Muhammad Khalidin; Furqan, Mhd; Fadlan, Aulia (Jurnal Teknik Informatika (Jutif), 2025)
Lung diseases remain a major global health challenge, requiring accurate and interpretable diagnostic systems to support timely detection and treatment. This study proposes a high-fidelity deep learning approach using the ConvNeXt architecture for automated multi-class classification of chest X-ray (CXR) images into five categories: Bacterial Pneumonia, Viral Pneumonia, COVID-19, Tuberculosis, and Normal. The methodology involved preprocessing 10.095 Kaggle-sourced images (normalization, CLAHE, augmentation, resizing) and training a ConvNeXt model for 70 epochs with the Adam optimizer. The model achieved strong performance with 92.66% validation accuracy, 86.32% test accuracy, a macro-average F1-score of 0.86, and a macro-average AUC of 0.99. Grad-CAM visualizations demonstrated the model's consistent focus on clinically relevant lung regions, significantly improving interpretability and clinical applicability. This study contributes to advancing interpretable AI methods for clinical decision support in medical imaging, offering a reliable and transparent framework for automated lung disease diagnosis.