Alzheimer’s Disease Segmentation and Classification on MRI Brain Images Using Enhanced Expectation Maximization Adaptive Histogram (EEM-AH) and Machine Learning.
نویسندگان
چکیده
Alzheimer’s disease (AD) is an irreversible ailment. This ailment causes rapid loss of memory and behavioral changes. Recently, this disorder very common among the elderly. Although there no specific treatment for disorder, its diagnosis aids in delaying spread disease. Therefore, past few years, automatic recognition AD using image processing techniques has achieved much attraction. In research, we propose a novel framework classification magnetic resonance imaging (MRI) data. Initially, filtered 2D Adaptive Bilateral Filter (2D-ABF). The denoised then enhanced Entropy-based Contrast Limited Histogram Equalization (ECLAHE) algorithm. From data, region interest (ROI) segmented clustering thresholding techniques. Clustering performed Enhanced Expectation Maximization (EEM) (AH) ROI, Gray Level Co-Occurrence Matrix (GLCM) features are generated. GLCM feature that computes occurrence pixel pairs spatial coordinates image. dimension these reduced Principle Component Analysis (PCA). Finally, obtained classified classifiers. work, have employed Logistic Regression (LR) classification. results were with accuracy 96.92% from confusion matrix to identify Disease. proposed was evaluated performance evaluation metrics like accuracy, sensitivity, F-score, precision specificity arrived matrix. Our study demonstrates detection model outperforms other models literature.
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ژورنال
عنوان ژورنال: Information Technology and Control
سال: 2022
ISSN: ['1392-124X', '2335-884X']
DOI: https://doi.org/10.5755/j01.itc.51.4.28052