XAI-LENS: An Explainable AI–Based Classification of Lung Disorders from X-ray Images

Abstract

Accurate and efficient classification of lung diseases from chest X-ray (CXR) images is vital for timely diagnosis and treatment, particularly in healthcare environments with limited resources. This study introduces LightXrayNet, a lightweight convolutional neural network designed for multi-class classification of CXR images into nine categories: Normal, Pneumonia, Higher Density, Lower Density, Obstructive Pulmonary Diseases, Degenerative Infectious Diseases, Encapsulated Lesions, Mediastinal Changes, and Chest Changes. The dataset was sourced from the publicly available X-ray Lung Diseases Images (9 Classes) repository on Kaggle and subjected to a comprehensive preprocessing pipeline, including adaptive CLAHE-based contrast enhancement, resizing, normalization, light augmentation (horizontal flip, ±5° rotation), and data splitting. LightXrayNet’s performance was benchmarked against three pretrained CNNs—DenseNet201, ResNet50V2, and InceptionV3—using metrics such as accuracy, precision, recall, F1-score, confusion matrices, and training efficiency. Experimental results show that LightXrayNet achieved a test accuracy of 99.22% and near-perfect values across all classes, while requiring substantially less training time compared to deeper pretrained architectures. These findings demonstrate the potential of LightXrayNet as a practical and deployable solution for automated lung disease detection, with strong applicability in resource-constrained healthcare settings.