Automated Detection and Classification of Spinal Stenosis for Enhanced Accuracy

DOI

10.2991/978-94-6463-754-0_47How to use a DOI?

Keywords

Spinal Stenosis; AI; Convolutional Neural Networks (CNN); ResNet-50 v2; Feature Extraction; Neural Networks in Medical Imaging; Automated Diagnosis

Abstract

Spinal stenosis is a constant condition that limits the spinal canal, compressing nerves and causing neurological side effects. A convenient and exact conclusion is basic for the result of treatment and care. This study analyzes the advancement of an computerized framework that employments progressed machine learning and therapeutic imaging procedures to recognize and categorize spinal stenosis. The proposed strategy investigations MRI and CT filter information and effectively recognizes stenotic regions utilizing deep learning strategies, such as convolutional neural systems (CNNs). Higher determination precision is gotten by making strides include extraction and classification through the utilize of picture preparing strategies. The machine-learning approach makes a difference specialists make solid choices, moves forward early determination, and diminishes mistakes in individual evaluations. When compared to ordinary demonstrative strategies, test comes about appear that the show has an extraordinary level of precision, affectability, and specificity in identifying spinal stenosis. This study appears how manufactured insights has the potential to progress understanding results, diminish clinical delays, and alter the determination of Spinal stenosis.

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.

Download article (PDF)

TY  - CONF
AU  - Pata Chethan Reddy
AU  - Yarramaddu Dhathri Desai
AU  - Murugaveni Sudamani
PY  - 2025
DA  - 2025/06/30
TI  - Automated Detection and Classification of Spinal Stenosis for Enhanced Accuracy
BT  - Proceedings of the 2025 International Conference on Advanced Research in Electronics and Communication Systems (ICARECS-2025)
PB  - Atlantis Press
SP  - 534
EP  - 543
SN  - 2589-4943
UR  - https://doi.org/10.2991/978-94-6463-754-0_47
DO  - 10.2991/978-94-6463-754-0_47
ID  - Reddy2025
ER  -