AI-Based Bone Tumor Detection

Abstract

Bone tumors are abnormal growths inside bones that can either be benign (non-cancerous) or malignant (cancerous). Benign tumors generally do not spread, whereas malignant tumors can metastasize to other parts of the body, becoming life-threatening. Therefore, early detection of bone tumors is crucial to improve survival rates and ensure timely medical intervention. Traditionally, bone tumors are diagnosed using X-rays, CT scans, and MRI scans, methods that depend heavily on expert radiologists for accurate interpretation. However, manual diagnosis is often time-consuming, subjective, and prone to errors due to fatigue, variations in expertise, and the complexity of medical images. With the advancement of Artificial Intelligence, Machine Learning (ML) and Deep Learning (DL) techniques provide modern and efficient approaches to medical image analysis. ML algorithms such as Support Vector Machines (SVM), Random Forest, and K-Nearest Neighbors (KNN) can be used for classification, while Convolutional Neural Networks (CNNs) are particularly effective in image recognition tasks.

Introduction

The project proposes an AI-based system for automatic bone tumor detection using medical images like X-rays. Traditional diagnosis relies on manual interpretation by radiologists, which can be slow and error-prone. To improve this, the system uses Convolutional Neural Networks (CNNs) to detect tumors and classify them as Normal, Benign, or Malignant, providing faster and more accurate results.

The system extends beyond basic detection by introducing a multi-modal framework that combines image analysis with clinical data, and a smart recommendation engine that suggests further tests, treatments, or specialist referrals based on predictions. It also generates detailed outputs including confidence scores, probability distributions, highlighted tumor regions, and structured diagnostic reports for better decision-making and record-keeping.

The architecture includes modules for image preprocessing, feature extraction, classification, result generation, and storage. A user-friendly interface allows radiologists to upload images and view results, while the backend handles processing and database management.

The methodology follows a pipeline: data collection → preprocessing → CNN-based analysis → classification → report generation. Data flow diagrams explain how information moves through different system levels.

Results show that the system effectively identifies abnormalities, reduces diagnostic effort, improves accuracy, and supports radiologists in clinical decision-making.

Overall, the proposed system acts as a smart diagnostic and decision-support tool, enhancing efficiency, consistency, and early detection in medical imaging.

References

[1] J. He, X. Bi, \"Automatic classification of spinal osteosarcoma and giant cell tumor of bone using optimized DenseNet,\" Journal of Bone Oncology, 2024, https://doi.org/10.1016/j.jbo.2024.100606. [2] J. Shao, \"Deep learning for differentiation of osteolytic osteosarcoma and giant cell tumor around the knee joint on radiographs: a multicenter study,\" Insights into Imaging, 2024, https://doi.org/10.1186/s13244-024-01610-1. [3] B. D. Rao, K. Madhavi, \"Enhancing bone cancer detection through optimized pre-trained deep learning models and explainable AI using the osteosarcoma tumor assessment dataset,\" Scientific Reports, 2025, https://doi.org/10.1038/s41598-025-26051-8. [4] Q. Zhao, \"Feasibility of machine learning–based modeling and prediction to assess osteosarcoma outcomes,\" Scientific Reports, 2025, https://doi.org/10.1038/s41598-025-00179-z. [5] M. D. Nguyen, \"Bridging classification and segmentation in osteosarcoma assessment via foundation and discrete diffusion models,\" arXiv preprint, arXiv:2501.01932, 2025, https://arxiv.org/abs/2501.01932. [6] Y. Chen, \"Radiomics-integrated deep learning with hierarchical loss for osteosarcoma histology classification,\" arXiv preprint, arXiv:2601.09416, 2026, https://arxiv.org/abs/2601.09416.

Copyright

Copyright © 2026 Kedar Dhananjay Kavade, Jay Ganesh Gaikwad, Vaibhav Vinod Kamble, Tanmay Anil Gawande, Avishkar Devidas Bichukale, Dr. S. S. Kulkarni. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.