Evaluation of Parkinson Disease from MRI Images Using Deep Learning Techniques

Gloria F. Nkondo (), U. Snekhalatha () and Shubashini Rathina Velu
Additional contact information
Gloria F. Nkondo: SRM Institute of Science and Technology
U. Snekhalatha: SRM Institute of Science and Technology
Shubashini Rathina Velu: Prince Mohammad Bin Fahd University

A chapter in Machine Learning and Deep Learning Modeling and Algorithms with Applications in Medical and Health Care, 2025, pp 99-116 from Springer

Abstract: Abstract Purpose (i) To develop a lightweight custom model named Parkinson disease (PD) Net for the categorization of PD and normal brain image and compare its performance with VGG16 and VGG19 models in Magnetic Resonance imaging (MRI) images; (ii) to extract statistical textural and shape features and perform feature fusion with automated features and perform further classification. Materials and Methods In study, a dataset consisting of 30 MRI brain images (15 Parkinson’s positive (PP), and 15 Parkinson’s negative (NP)) in DICOM format was utilized. Additionally, 170 MRI brain images (85 PP, 85 NP) sourced from the International Neuroimaging Data share were also included in the analysis. The MRI brain image underwent segmentation through the skull stripping method, and subsequent statistical and brain features was extracted. The automated features derived from the PD Net models features were combined with extracted statistical features. The fused feature set was utilized for the classification into Parkinson’s disease and normal cases. Results The lightweight custom model named PD Net demonstrated superior performance compared to the VGG16 and VGG19 models in the classification of Parkinson’s atrophy from the brain MRI image dataset. The accuracy of the PD Net was 98%, whereas VGG19 and VGG16 models attained accuracy of 96%, and 91% respectively. Additionally, the sensitivity and specificity, for VGG19 were consistently higher than those of VGG16 and VGG19. In conclusion, the developed system proved to be efficacious in precisely categorizing Parkinson’s brain atrophy using the VGG19 classifier.

Keywords: Parkinson; Brain atrophy; Skull-stripping segmentation; Brain symmetry; MRI (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:ssrchp:978-3-031-98728-1_6

Ordering information: This item can be ordered from
http://www.springer.com/9783031987281

DOI: 10.1007/978-3-031-98728-1_6

Access Statistics for this chapter

More chapters in Springer Series in Reliability Engineering from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().