An equal-weight soft-voting CNN ensemble with explainable AI for three-class knee radiograph classification
4 affiliations
- 1Department of Computer Science and Engineering, Daffodil International University
- 2Institute of Information Technology, Jahangirnagar University
- 3Computer Science & Information Technology, College of Engineering, Abu Dhabi University
- 4IT Department, Yoobee Colleges of Creative Innovation
Abstract
Osteoporosis is a common metabolic bone disorder associated with reduced bone strength and increased fracture risk. Although Dual-Energy X-ray Absorptiometry (DEXA) is the clinical standard for bone mineral density assessment, its cost and limited availability can restrict its use in resource-constrained settings. This study presents an explainable deep learning framework for three-class radiographic pattern classification using standard knee X-ray images. Two publicly available datasets were combined, yielding 2,186 radiographs labeled as Normal, Osteopenia, and Osteoporosis. A preprocessing pipeline comprising morphological opening, contrast-limited adaptive histogram equalization, and intensity normalization was applied, followed by geometric augmentation that expanded the dataset to 8,744 image observations. Eight pretrained CNN architectures were evaluated, and an equal-weight soft-voting ensemble comprising DenseNet201, MobileNetV2, and InceptionV3 was constructed by averaging their predicted class probabilities. The ensemble achieved an accuracy of 98.17% on the augmented experimental test partition and achieved the highest observed performance among the evaluated individual and ensemble configurations. LIME and Integrated Gradients were employed to provide qualitative visualizations of image regions associated with selected predictions. A prototype web application was also developed for individual and batch image classification. Because the labels were inherited from public radiographic repositories and were not directly linked to verified DEXA-derived measurements, the framework should be interpreted as a repository-label-based radiographic pattern classification system rather than a method for quantitative bone mineral density estimation. Furthermore, the reported results represent internal evaluation on the constructed augmented dataset and do not establish independent patient-level or clinical generalization.
Related
- Explainable DSC Perfusion Radiomics for Post-Treatment Glioblastoma Assessment Using a Soft-Voting Machine-Learning Ensemble
- Skin Cancer Classification Using Explainable Artificial Intelligence With an Ensemble Model and Rigorous Leakage Free Validation
- Deep learning grading of Schistosoma japonicum-associated liver fibrosis from ultrasound images: a weighted soft-voting ensemble of convolutional and transformer networks
- Hybrid Explainable Deep Ensemble Framework with self-attention mechanism for early prediction of depression severity using PHQ-9 clinical and behavioral data
- Explainable diabetes prediction using a stacked ensemble framework
BibTeX
@article{tonni2026equalweight,
title = {{An equal-weight soft-voting CNN ensemble with explainable AI for three-class knee radiograph classification}},
author = {Sadia Islam Tonni and Md. Alif Sheakh and Mst. Sazia Tahosin and Jesmin Akhter and Modafar Ati and Arshad Khan},
journal = {Scientific Reports},
year = {2026},
month = aug,
doi = {10.1038/s41598-026-66327-1},
url = {https://doi.org/10.1038/s41598-026-66327-1},
note = {CC BY},
}
RIS
TY - JOUR
TI - An equal-weight soft-voting CNN ensemble with explainable AI for three-class knee radiograph classification
AU - Sadia Islam Tonni
AU - Md. Alif Sheakh
AU - Mst. Sazia Tahosin
AU - Jesmin Akhter
AU - Modafar Ati
AU - Arshad Khan
PY - 2026
DA - 2026/08/01
JO - Scientific Reports
DO - 10.1038/s41598-026-66327-1
UR - https://doi.org/10.1038/s41598-026-66327-1
AB - Osteoporosis is a common metabolic bone disorder associated with reduced bone strength and increased fracture risk. Although Dual-Energy X-ray Absorptiometry (DEXA) is the clinical standard for bone mineral density assessment, its cost and limited availability can restrict its use in resource-constrained settings. This study presents an explainable deep learning framework for three-class radiographic pattern classification using standard knee X-ray images. Two publicly available datasets were combined, yielding 2,186 radiographs labeled as Normal, Osteopenia, and Osteoporosis. A preprocessing pipeline comprising morphological opening, contrast-limited adaptive histogram equalization, and intensity normalization was applied, followed by geometric augmentation that expanded the dataset to 8,744 image observations. Eight pretrained CNN architectures were evaluated, and an equal-weight soft-voting ensemble comprising DenseNet201, MobileNetV2, and InceptionV3 was constructed by averaging their predicted class probabilities. The ensemble achieved an accuracy of 98.17% on the augmented experimental test partition and achieved the highest observed performance among the evaluated individual and ensemble configurations. LIME and Integrated Gradients were employed to provide qualitative visualizations of image regions associated with selected predictions. A prototype web application was also developed for individual and batch image classification. Because the labels were inherited from public radiographic repositories and were not directly linked to verified DEXA-derived measurements, the framework should be interpreted as a repository-label-based radiographic pattern classification system rather than a method for quantitative bone mineral density estimation. Furthermore, the reported results represent internal evaluation on the constructed augmented dataset and do not establish independent patient-level or clinical generalization.
SN - 2045-2322
ER -