Enhancing Alzheimer's Disease Detection Through Advanced Image Processing Techniques
Abstract
This study proposes a new method for Alzheimer’s Disease detection using 3D brain MR images and first-order statistical features. Alzheimer’s is a progressive neurodegenerative disorder affecting the elderly. The method focuses on extracting features from grey and white matter to predict AD using ensemble classifiers.
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Introduction
Alzheimer’s Disease (AD) is a progressive neurodegenerative condition affecting primarily the elderly, with no known cure. Early detection is crucial for managing the disease. Computer-Aided Diagnostics utilizes advanced algorithms for identifying Features of Interest in MR images. As the global population ages, dementia cases, including AD, are expected to rise significantly. This review provides insights into AD and mild cognitive impairment (MCI), emphasizing clinical aspects, biomarkers for diagnosis, and ongoing therapeutic developments.
Conclusion
Alzheimer’s Disease (AD) affects the elderly and lacks a cure. With cases set to rise, early detection is crucial. Our article proposes an image processing-based method using Machine Learning Techniques. Gaussian filtering is applied in preprocessing, FCM in segmentation, and an Ensemble model in classification. Results show improved performance compared to existing methods.