Hybrid Deep Learning and Ensemble Model for Accurate Detection and Classification of Bone Cancer

Abstract

For bone cancer to be effectively treated and for patient survival to increase, early identification and precise categorisation are essential. This paper suggests a hybrid deep learning and ensemble model that combines ensemble learning for reliable classification with convolutional neural networks (CNN) for feature extraction. The model handles issues like data imbalance and complicated bone textures when processing medical pictures like MRI scans and X-rays. According to experimental results, the suggested strategy outperforms current methods with an accuracy of 94.7 %, precision of 94.1 %, recall of 93.8 %, F1-score of 93.9 %, and an AUC of 0.97. These results demonstrate how hybrid models can offer accurate, automated bone cancer detection, assisting physicians in making diagnoses and enhancing patient outcomes.

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