Bone Cancer Detection using Hybrid Machine Learning And Deep Learning: A survey

DOI

10.2991/978-94-6239-745-3_14How to use a DOI?

Keywords

Bone Cancer; Deep Learning; Machine Learning; X-ray dataset; Optimization

Abstract

Bone cancer, predominantly primary bone tumors, is a heterogeneous group of malignant neoplasms that originate in the skeletal system. Bone cancer is an oncological disease that affects bone cells and is characterized by some abnormal cells in the bone tissue begin to grow uncontrollably forming malignant neoplasms. Even though this type of disease is rare, such tumors represent a significant diagnostic and therapeutic challenge due to their diversity and the similarity of many of their symptoms to signs of other bone diseases, such as infections. In the United States, this type of tumor is the third leading cause of cancer death in individuals younger than twenty years. The main types of primary bone cancer include: osteosarcoma, chondrosarcoma, Ewing sarcoma Medical imaging techniques such as X-rays, MRI, computed tomography, and biopsy is performed for diagnosis. Early diagnosis significantly increases the chances of successful treatment and long-term remission. This survey presents a comprehensive systematic review of studies and research conducted in the domain of machine learning approaches, deep learning algorithms, the utilization of optimization algorithms for bone cancer detection and classification. Deep learning (DL), a branch of artificial intelligence, has emerged as an effective tool for analyzing medical images and classifying diseases. Deep learning models, particularly convolutional neural networks (CNNs), have demonstrated an exceptional ability to automatically extract hierarchical features from medical images, enabling the accurate detection and classification of bone tumors. These models can learn complex patterns and subtle variations in imaging data that may be difficult for human observers to discern, thus enhancing diagnostic accuracy and efficiency.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits any noncommercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if you modified the licensed material. You do not have permission under this license to share adapted material derived from this chapter or parts of it.

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TY  - CONF
AU  - Yaqeen Ali Mohsin
AU  - Osama Majeed Hilal
AU  - Alaa Taima Albu-Salih
PY  - 2026
DA  - 2026/08/24
TI  - Bone Cancer Detection using Hybrid Machine Learning And Deep Learning: A survey
BT  - Proceedings of the International Conference on Research, Innovation, Sustainability, and Educations (IRISECON 2026)
PB  - Atlantis Press
SP  - 208
EP  - 225
SN  - 3091-4442
UR  - https://doi.org/10.2991/978-94-6239-745-3_14
DO  - 10.2991/978-94-6239-745-3_14
ID  - Mohsin2026
ER  -