Improved Deep Joint Segmentation with Enhanced Feature Set for Cervical Spine Fracture Classification: A Comprehensive Literature Review

DOI

10.2991/978-94-6463-858-5_257How to use a DOI?

Keywords

Cervical Spine Fracture; Deep Learning; Joint Segmentation; Feature Extraction; Spinal Cord Injury; Hybrid Neural Networks; Medical Imaging

Abstract

Cervical spine fractures are critical medical conditions that demand rapid and accurate diagnosis due to their potential to cause severe neurological impairment or death. Traditional diagnostic methods relying on manual radiographic interpretation are often limited by subjectivity and time constraints. With the growing prominence of artificial intelligence (AI) and deep learning, particularly in medical image analysis, automated methods for cervical spine fracture detection have emerged as powerful alternatives. This literature review systematically explores and critically evaluates state-of-the-art techniques in cervical spine injury detection and classification, focusing on segmentation-based architectures, hybrid classifiers, and deep feature extractors. Special emphasis is placed on models that employ improved joint segmentation and enhanced feature sets, such as Improved Median Binary Patterns (MBP), Local Gabor XOR Patterns (LGXP), and deep hybrid classifiers including PCNN and Bi-GRU. This review categorizes and compares methodologies from recent high-impact studies, identifies research gaps, and highlights future directions such as 3D morphological analysis, explainability in AI models, and integration of multi-modal imaging. Our findings emphasize the importance of precision, automation, and clinical applicability in developing next-generation cervical spine classification systems.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, 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 changes were made.

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TY  - CONF
AU  - K. Goutham Raju
AU  - S. Ravikumar
PY  - 2025
DA  - 2025/11/04
TI  - Improved Deep Joint Segmentation with Enhanced Feature Set for Cervical Spine Fracture Classification: A Comprehensive Literature Review
BT  - Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
PB  - Atlantis Press
SP  - 3076
EP  - 3089
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-858-5_257
DO  - 10.2991/978-94-6463-858-5_257
ID  - Raju2025
ER  -