This paper presents a comprehensive approach for the detection, segmentation, and classification of bone tumors in medical imaging using the Fast Mask R-CNN framework. Bone tumors pose significant challenges in diagnosis and treatment planning due to their diverse morphological characteristics and potential for malignancy. The proposed methodology aims to address these challenges by leveraging deep learning techniques to achieve precise localization and classification of bone tumors within radiographic images. By integrating object detection and segmentation capabilities, the proposed model enables accurate delineation of tumor boundaries while facilitating multi-class classification to differentiate between benign and malignant tumors. Extensive experimental evaluations on a diverse dataset demonstrate the effectiveness and robustness of the proposed approach in accurately detecting and classifying bone tumors. This research contributes to advancing computer-aided diagnosis systems in orthopedic imaging, offering a powerful tool for improving diagnostic accuracy and clinical decision-making in bone tumor management.