Intelligent MRI Analysis for Parkinson’s Disease Detection
DOI:
https://doi.org/10.6000/Keywords:
MRI Classification, Computer Aided Diagnosis, SIFT, LBP, KNNAbstract
This study presents a practical approach for classifying Magnetic Resonance Imaging (MRI) scans to distinguish between normal subjects and those affected by Parkinson’s disease (PD). PD is a progressive brain disorder marked by dopamine deficiency, and lacks reliable diagnostic methods for early detection. To overcome this challenge, we employed Scale-Invariant Feature Transform (SIFT) and Local Binary Pattern (LBP) in designing a Computer-Aided Diagnostic (CAD) System. The extracted features are classified using K-Nearest Neighbour (KNN) and Decision Tree algorithms. Experimental results show that LBP features classified through the Decision Tree achieved the highest accuracy of 97.41%, demonstrating the efficiency of the proposed method in achieving early and accurate detection of PD.
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