Machine Learning Based Analysis of Structural MRI for Epilepsy Diagnosis
Abstract
Epilepsy is a common neurological disorder, charactererized by abnormal firing of neurons. Magnetic Resonance Imaging (MRI) techniques can be integrated with machine learning methods to diagnose epileptic patients noninvasively. In this study, we use structural MRI data of 17 subjects (10 epileptic patients and 7 normal control subjects) and segment brain tissues using a Gram-Schmidt orthogonalization method and a unified tissue segmentation approach. We then compute first-order statistical and volumetric gray-level co-occurrence matrix (GLCM) texture features and train SVM classifiers for epilepsy diagnosis based on the features of the whole brain or those of the hippocampus. We achieve an accuracy of 94 % using the unified segmentation method and whole-brain analysis approach.
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Sahebzamani et al. [163] achieved 94% accuracy for epilepsy diagnosis.
“This comprehensive segmentation and whole-brain analysis methodology yielded a 94% accuracy rate.”
From A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis (2024) · cites this paper as [163]
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References 19 · Cited by 8
- Authors
- Sahebzamani, G., Saffar, M., & Soltanian-Zadeh, H.
- Affiliations
- University of Tehran (IR)
- Venue
- 2019 4th International Conference on Pattern Recognition and Image Analysis (IPRIA)
- Publisher
- IEEE
- Published
- 2019-03
- Pages
- 58-63
- OpenAlex
- W2966680208
- Cited by
- 8
- References
- 19
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 2 · 2023: 1 · 2022: 1 · 2021: 2
- FWCI
- 0.44
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Topics
- Life Sciences › Neuroscience › Neurology › Brain Tumor Detection and Classification
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- epilepsy diagnosis, structural MRI, brain tissue segmentation, Gram-Schmidt orthogonalization, gray level co-occurrence matrix, texture features, support vector machine, hippocampus, whole-brain analysis, automated diagnosis
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