Lumbar Spine Degenerative Classification

Abstract: Lumbar Spine Degenerative Classification is a deep learning-based initiative focused on the automated analysis of MRI images to detect and categorize spinal disc degeneration. The system classifies images into three levels: normal, mild/moderate, and severe, helping reduce subjectivity in medical diagnosis. By applying an efficient convolutional neural network architecture, the model captures subtle features in spinal structures, leading to improved diagnostic accuracy. This project aims to support healthcare professionals by offering consistent, fast, and reliable assessments of lumbar spine health. It enhances clinical workflows, encourages early detection, and facilitates ongoing monitoring. With a focus on scalability and accessibility, the system can be integrated into medical settings to improve patient care outcomes. The project demonstrates the potential of artificial intelligence in transforming medical imaging and contributes to building a more intelligent and supportive healthcare environment.

Authors

Publication Details

Journal
International Journal for Research in Applied Science and Engineering Technology
Published
2025-04-17
DOI
https://doi.org/10.22214/ijraset.2025.68905
Primary Topic
Medical Imaging and Analysis
Type
article
Field-Weighted Citation Impact
0.00
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article

Lumbar Spine Degenerative Classification

Asst. Prof. Bhagya K
International Journal for Research in Applied Science and Engineering Technology
Medical Imaging and Analysis
article

Lumbar Spine Degenerative Classification

Asst. Prof. Bhagya K
article en

Abstract

Abstract: Lumbar Spine Degenerative Classification is a deep learning-based initiative focused on the automated analysis of MRI images to detect and categorize spinal disc degeneration. The system classifies images into three levels: normal, mild/moderate, and severe, helping reduce subjectivity in medical diagnosis. By applying an efficient convolutional neural network architecture, the model captures subtle features in spinal structures, leading to improved diagnostic accuracy. This project aims to support healthcare professionals by offering consistent, fast, and reliable assessments of lumbar spine health. It enhances clinical workflows, encourages early detection, and facilitates ongoing monitoring. With a focus on scalability and accessibility, the system can be integrated into medical settings to improve patient care outcomes. The project demonstrates the potential of artificial intelligence in transforming medical imaging and contributes to building a more intelligent and supportive healthcare environment.

International Journal for Research in Applied Science and Engineering TechnologyVol. 13(4)
Openalex Percentile: Top 93%
Medical Imaging and Analysis
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