Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Mukku, Lalasa; Burri, Vikas; Lamani, Manjunath Ramanna Title A name given to the resource Artificial Intelligence-Driven Lumbar Stenosis Diagnosis: A Deep Learning Pipeline for MRI-Based Segmentation and Classification Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived 2025 IEEE 4th World Conference on Applied Intelligence and Computing, AIC 2025;pp.240-244 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/AIC66080.2025.11211995" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/AIC66080.2025.11211995</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105023638615?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105023638615?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant 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 Description An account of the resource 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. Subject The topic of the resource artificial intelligence; disc degeneration; Lumbar spine; SegNet; spinal stenosis; swin transformer Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833152613-9; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online