Deep Dive into Bone Tumor Segmentation and Classification: Methodological Review and Challenges with Deep Learning Approaches

This comprehensive review delves into the advancements made in utilizing Deep Learning (DL) procedures for bone tumor separation and classification. Bone tumors present a complex challenge in medical imaging due to their diverse morphological characteristics and potential for malignant behaviour. Traditional methods for tumor analysis often require extensive manual intervention and lack the efficiency needed for clinical applications. Deep learning approaches, with the accessibility of large-scale medical imaging datasets and sophisticated computer resources, have emerged as intriguing alternatives to solve these constraints. In this connection an attempt is made to review synthesizes recent developments in deep learning architectures, tailored specifically for bone tumor segmentation and classification tasks. Additionally, it examines the challenges associated with data acquisition, preprocessing, and annotation, along with strategies to mitigate them. Furthermore, it discusses the integration of multimodal imaging modalities, to improve efficiency and reliability of tumor characterization. The review also surveys benchmark dataset sand various strategies commonly employed in this domain. As a result, propose future directions for advancing the field of bone tumor analysis using deep learning methodologies.