Conference Paper2025Cited by 2

Detection-Guided Lumbar Spine Degeneration Classification Using Multi-Slice Encoded MRI

Samiul Karim Mazumder, Riyadul Islam, Siam Tahsin Bhuiyan, Rashedur Rahman, Sefatul Wasi, Ashraful Islam, Saadia Binte Alam

2025 International Conference on Machine Learning and Cybernetics (ICMLC)

IEEE, pp. 75-80

  • Good Health and Well-being — Relevance: 94%
DOI: 10.1109/icmlc66258.2025.11280017URL

Abstract

Lumbar Spine Degeneration Classification (LSDC) poses significant diagnostic challenges as radiologists manually assess each MRI scan which is time-consuming, subjective, and prone to inter-observer variability. This highlights the need for an automated, objective, and efficient diagnostic solution to assist radiologists. Current deep learning-based approaches rely on single-slice MRI scans, which lack contextual spatial information. To address these challenges, we propose a Detection-Guided Classification Framework that integrates automated region detection with deep learning-based severity classification. Our approach introduces a novel RGB-based pre-processing technique that encodes multi-slice Sagittal T2-weighted MRI information into a single image. We first detect the degenerative regions to ensure that the classification models focus on the relevant areas. We then train multiple Convolutional Neural Network models to classify degeneration severity. Using the RSNA dataset, the YOLOv8 detection model achieves a mAP50 score of 0.9905. In the classification models, ResNet152 scores the highest accuracy of 92.74%, and EfficientNetB5 achieves the highest accuracy of 88.18% in the holdout test. Our result suggests that using the multi-slice encoded MRI dataset, the proposed detection-guided classification framework effectively localizes degenerative regions and achieves high classification accuracy by focusing on relevant areas rather than processing entire MRI slices.

Keywords

  • lumbar degenerative disease
  • YOLOv8
  • convolutional neural networks

CCDS Authors

Riyadul Islam
Riyadul Islam

Riyadul Islam

  • Intern, CCDS

Bachelors

Daffodil International University

Published at

Scientific Reports

Siam Tahsin Bhuiyan
Siam Tahsin Bhuiyan

Siam Tahsin Bhuiyan

  • Intern, CCDS

Bachelors

Independent University, Bangladesh

Published at

Scientific Reports

Md. Rashedur Rahman, D. Eng.
Md. Rashedur Rahman, D. Eng.

Md. Rashedur Rahman, D. Eng.

  • Co-Director, MIRA & NEST Wings

Assistant Professor

Department of Computer Science and Engineering

Independent University, Bangladesh

Published at

CHI, HCII, Scientific Reports, Cognitive Computation, Neural Computing and Applications

Ashraful Islam, PhD
Ashraful Islam

Ashraful Islam, PhD

  • Center Director, CCDS
  • Director, HCI Wing

Assistant Professor

Department of Computer Science and Engineering

Independent University, Bangladesh (IUB)

Published at

CHI, UIST, UbiComp, IJCNN, ICIP, HCII, ICDMW, BLP, IEEE Internet of Things Journal, JMIR, JMIR Human Factors, JMIR Formative Research, IEEE Access, Smart Agricultural Technology, Diabetes Research and Clinical Practice, PLOS ONE

Research Interests

Human-Computer Interaction, AI for Social Good, AI for Public Health, AI for Impact

Saadia Binte Alam, PhD
Saadia Binte Alam, PhD

Saadia Binte Alam, PhD

  • Director, MIRA & WiSE Wings

Associate Professor

Department of Computer Science and Engineering

Independent University, Bangladesh

Published at

HCII, Scientific Reports, Cognitive Computation, Neural Computing and Applications

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  2. 2.Rani Puspita, Thoyyibah T. (2025). Impact of Model Architecture and Optimizer on MRI-Based Detection of Alzheimer’s Disease: EfficientNetB1, EfficientNetB5, and NASNet-Large. , 596–601[10.1109/icsintesa68165.2025.11413624]
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