Enhanced Multi-Class Pulmonary Disorder Detection Using Hard Voting Ensemble of CNN Models on X-Ray Images

Authors

Ebeid Ali Ebeid
Farida Youness
DOI: https://doi.org/10.52549/ijeei.v13i3.6606
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Published: 14-09-2025 • Issue: Vol. 13 No. 3: September 2025 • Section: Regular paper

Abstract

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.
Keywords: Chest X-Ray; Efficientnetb0; Densenet-201; Resnet50; MobilenetV2; InceptionnetV3

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How to Cite

[1]
E. A. Ebeid and F. Youness, “Enhanced Multi-Class Pulmonary Disorder Detection Using Hard Voting Ensemble of CNN Models on X-Ray Images”, IJEEI, vol. 13, no. 3, pp. 614–633, Sep. 2025, doi: 10.52549/ijeei.v13i3.6606.