ItemConference Paper

Artificial Intelligence-Driven Lumbar Stenosis Diagnosis: A Deep Learning Pipeline for MRI-Based Segmentation and Classification

Title
Artificial Intelligence-Driven Lumbar Stenosis Diagnosis: A Deep Learning Pipeline for MRI-Based Segmentation and Classification
Creator
Mukku, Lalasa; Burri, Vikas; Lamani, Manjunath Ramanna
Description
Lumbar spinal stenosis is a prevalent musculoskeletal disorder that requires accurate diagnosis through magnetic resonance imaging (MRI). However, manual interpretation of MRI images is time-consuming and subject to inter-observer variability. This study proposes an automated deep learning-based pipeline for lumbar stenosis identification, integrating advanced methodologies for preprocessing, segmentation, feature extraction, and classification. The pipeline consists of Super-Resolution Convolutional Neural Network (SRCNN) for MRI image enhancement, SegNet for segmentation of the spinal canal, intervertebral discs (IVDs), and neural foramen, Convolutional Block Attention Module (CBAM) for feature refinement, and Swin Transformer for final classification. The proposed method was evaluated on a publicly available multicenter lumbar spine MRI dataset, comprising 218 patient studies with 447 MRI series. Model performance was assessed using accuracy, recall, precision, and F1-score, achieving 95.2% accuracy, 89.82% recall, 92.3% precision, and an F1-score of 96.12%. The results demonstrate that SRCNN enhances MRI quality for improved segmentation, CBAM strengthens feature extraction, and Swin Transformer effectively classifies stenosis cases. This study highlights the efficacy of AI-driven methodologies in lumbar spine MRI analysis, offering a potential computer-aided diagnosis (CAD) tool for clinical applications. Future work may focus on optimizing model efficiency and improving generalization across diverse imaging protocols. 2025 IEEE.
Source
2025 IEEE 4th World Conference on Applied Intelligence and Computing, AIC 2025;pp.240-244
Date
01-01-2025
Publisher
Institute of Electrical and Electronics Engineers Inc.
Subject
artificial intelligence; disc degeneration; Lumbar spine; SegNet; spinal stenosis; swin transformer
Coverage
Mukku L., CHRIST University, Department of Computer Science and Engineering, Bangalore, India; Burri V., Colorado State University, CS, Fort Collins, United States; Lamani M.R., Moodlakatte Institute of Technology, Department of Computer Science and Engineering, Karnataka, Kundapura, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 979-833152613-9;
Format
online
Language
English
Type
Conference paper

Citation

Mukku, Lalasa; Burri, Vikas; Lamani, Manjunath Ramanna, “Artificial Intelligence-Driven Lumbar Stenosis Diagnosis: A Deep Learning Pipeline for MRI-Based Segmentation and Classification,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/25742.

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