Intelligent MRI Analysis for Parkinson’s Disease Detection

R.  Indhumathi; Alaa A.  ELnazer; Sharvari R.  Shukla; Showkat A.  Dar; Hela  Hakim; A. Vijaya  Mahendra Varman; Tanvir Habib  Sardar; Aafaq A.  Rather

doi:10.6000/1929-6029.2025.14.67

Authors

R. Indhumathi Department of AI/ML, Department of Computer Science, Idhaya College for Women, Kumbakonam, India
Alaa A. ELnazer Department of Marketing, College of Business, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia
Sharvari R. Shukla Symbiosis Statistical Institute, Symbiosis International (Deemed University), Pune, India
Showkat A. Dar Department of Computer Science and Engineering, GITAM University Bangalore Campus -561203, India
Hela Hakim Researcher Associated with the MIRACL Laboratory, University of Sfax, Tunisia-3029
A. Vijaya Mahendra Varman Department of Artificial Intelligence and Data Science, Panimalar Engineering College-600 123, Chennai, India
Tanvir Habib Sardar Department of CSE, School of Engineering, Dayananda Sagar University, Bengaluru–562112, India
Aafaq A. Rather Symbiosis Statistical Institute, Symbiosis International (Deemed University), Pune, India

DOI:

https://doi.org/10.6000/1929-6029.2025.14.67

Keywords:

MRI Classification, Computer Aided Diagnosis, SIFT, LBP, KNN

Abstract

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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