• Trupti Hande
• Trupti Hande
• Neha Jiwane
• vina Atkar
• Samiksha Pinagle

The rare aggressive disease of bone tumors needs both prompt identification and correct identification methods for better patient outcomes which help extend their life. Medical imaging and artificial intelligence (AI) technology advancements have brought significant progress in bone tumor detection and classification work. The review paper provides an extensive evaluation of machine learning and deep learning methods used for bone tumor identification through X-ray, MRI, CT, and histopathological image analysis. The research assesses different image preprocessing, segmentation, feature extraction, and classification methods while emphasizing their impact on convolutional neural networks (CNNs), transfer learning, ensemble models, and advanced transformer-based systems. The paper presents the advantages and drawbacks of standard machine learning classifiers when compared to deep learning models in three areas: accuracy measurement, generalization ability, and clinical use of the models. The research highlights existing challenges which include restricted annotated datasets and problems with class imbalance and difficulties in identifying tumor boundaries and interpreting results. The study investigates recent developments of multimodal learning, explainable AI, and radiomics integration. The review provides researchers and clinicians with organized information about existing methods and upcoming research areas which enable dependable automated bone tumor detection.

Bone tumor detection, Deep learning, Medical image analysis, Tumor segmentation, Computer-aided diagnosis etc

"A Systematic Review of Deep Learning Models for Bone Tumor Analysis from MRI Scan Data subtitile", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.13, Issue 3, page no.c780-c787, March-2026, Available :http://www.jetir.org/papers/JETIR2603300.pdf

"A Systematic Review of Deep Learning Models for Bone Tumor Analysis from MRI Scan Data subtitile", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.13, Issue 3, page no. ppc780-c787, March-2026, Available at : http://www.jetir.org/papers/JETIR2603300.pdf