Enhanced Multi-Class Pulmonary Disorder Detection Using Hard Voting Ensemble of CNN Models on X-Ray Images - Ebeid, Ebeid Ali; Youness, Farida (IAES Indonesian Section, 2025)
Lung diseases represent a major public health concern, requiring timely and accurate diagnosis. Chest X-rays are widely used for initial screening, but manual interpretation is time-consuming and subject to variability among radiologists. To address these challenges, this study presents an automated deep learning-based framework for multi-class lung disease detection. The proposed approach integrates five convolutional neural network (CNN) architectures—EfficientNetB0, DenseNet201, ResNet50, MobileNetV2, and InceptionV3—within a hard-voting ensemble classifier to improve diagnostic performance. Transfer learning is applied to extract deep features from chest X-ray (CXR) images, and the ensemble strategy enhances overall accuracy compared to individual models. The system was evaluated into six categories, including normal, COVID-19, tuberculosis, opacity, bacterial pneumonia, and viral pneumonia. Results demonstrate that the ensemble achieves approximately 97% accuracy, outperforming current state-of-the-art methods. Furthermore, the model shows strong capability in differentiating bacteria from viral pneumonia, underscoring its potential as a reliable tool for automated lung disease diagnosis in clinical practice.