Optimized Deep Learning Framework Based On EfficientNetB0- for Bone Cancer Detection
DOI:
https://doi.org/10.29304/jqcsm.2026.18.32843Keywords:
Machine Learning, , X-ray dataset, Bone Cancer, Deep Learning OptimizationAbstract
Bone cancer detection from X-ray images is a challenging task due to the subtle differences between normal and cancerous tissues and the variability in image quality. In this study, an efficient deep learning framework is proposed for the automatic classification of bone cancer using radiographic images. The proposed model is based on the EfficientNetB0 architecture, which is employed as a feature extraction backbone ,To improve the model performance, a modified Whale Optimization Algorithm (WaOA) is utilized to optimize key hyperparameters, The training process is conducted in two stages: Stage A focuses on feature extraction with frozen backbone layers, while Stage B performs fine-tuning by unfreezing a subset of layers to enhance model generalization . The proposed approach is evaluated using standard performance metrics such as accuracy and area under the ROC curve (AUC).Experimental results demonstrate that the optimized EfficientNetB0 model achieves strong classification performance and provides a reliable solution for early bone cancer detection with high accuracy reached to 95.5% . The integration of metaheuristic optimization with deep learning significantly enhances model stability and predictive capability
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Copyright (c) 2026 Yaqeen Ali Mohsin , Osama Majeed Hilal, Alaa Taima Albu-Salih
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