Diagnosis of Alzheimer Diseases in Early Step Using SVM (support vector machine)
Abstract
Alzheimer is a disease that affects the brain. It causes degeneration of nerve cells (neurons) and in particular cells involved in memory and intellectual functions. Early diagnosis of Alzheimer Diseases (AD) raises ethical questions, since there is, at present, no cure to offer to patients and medicines from therapeutic trials appear to slow the progression of the disease as moderate, accompanying side effects sometimes severe. In this context, analysis of medical images became, for clinical applications, an essential tool because it provides effective assistance both at diagnosis therapeutic follow-up. Computer Assisted Diagnostic systems (CAD) is one of the possible solutions to efficiently manage these images. In our work, we proposed an application to detect Alzheimer's diseases. For detecting the disease in early stage we used the three sections: frontal to extract the Hippocampus (H), Sagittal to analysis the Corpus Callosum (CC) and axial to work with the variation features of the Cortex(C). Our method of classification is based on Support Vector Machine (SVM). The proposed system yields a 90.66% accuracy in the early diagnosis of the AD.
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Rabeh et al. (2016) used an SVM involving morphological features from the hippocampus, corpus callosum, and cortex to achieve 90.66% accuracy in early AD detection.
“Rabeh et al. (2016) utilized an SVM involving morphological features from the hippocampus, corpus callosum, and cortex to achieve 90.66% accuracy in early AD detection.”
From Multimodal neuroimaging and AI integration in cognitive disorders: advances, challenges, and future directions for precision medicine (2026) · cites this paper as Rabeh et al., 2016
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- Authors
- Rabeh, A. B., Benzarti, F., & Amiri, H.
- ORCID
- Benzarti, Faouzi
- Venue
- 2016 13th International Conference on Computer Graphics, Imaging and Visualization (CGiV)
- Publisher
- IEEE
- Published
- 2016-03
- Pages
- 364-367
- OpenAlex
- W2382818215
- Cited by
- 77
- References
- 0
- Citations by year
- 2026: 5 · 2025: 12 · 2024: 12 · 2023: 14 · 2022: 13 · 2021: 10 · 2020: 3 · 2019: 3 · 2018: 4 · 2017: 1
- FWCI
- 9.11
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Computer Graphics, Imaging and Visualization (CGIV) (publishedVersion)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Keywords
- Alzheimer's disease, early diagnosis, support vector machine, computer-aided diagnosis, hippocampus, corpus callosum, cerebral cortex, brain MRI segmentation, neurodegeneration
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