A Hybrid Particle Swarm Optimization-Neural Network Approach for Parkinson's Disease Diagnosis from MRI Images

Authors

  • Chinmay Phadtare Asst. Prof. Dept. of Neurosciences, Email Id: chinmay.phadtare@gmail.com, Krishna Vishwa Vidyapeeth Deemed To Be University, Karad.
  • Patange Aparna P. Department of General Medicine Krishna Vishwa Vidyapeeth, Karad, Maharashtra, India
  • Preethi Assistant Professor, School of Computing and Information Technology, REVA University, Bengaluru
  • Ashok Kumar Sahoo Department of Computer Science and Engineering, Graphic Era Hill University Dehradun, Uttarakhand, India,
  • Ankur Choudhary Department of Computer Science & Engineering, Graphic Era Deemed to be University, Dehradun, Uttarakhand, India, 248002

Keywords:

Parkinson Disease, PSO, Disease Diagnosis, Convolution Neural Network

Abstract

Given the complex interplay of motor and non-motor symptoms in Parkinson's disease (PD), early diagnosis is crucial for successful treatment. The use of magnetic resonance imaging (MRI) to identify structural brain abnormalities linked to Parkinson's disease has shown promise. In this paper, we suggest a novel Hybrid Particle Swarm Optimization-Neural Network (PSO-NN) method for rapid and reliable PD detection utilising MRI data.To improve the accuracy of PD diagnosis, our PSO-NN model combines the optimisation potential of Particle Swarm Optimisation (PSO) and the prediction strength of a Neural Network (NN). In order to create the best possible configuration for feature extraction and classification, PSO is used to fine-tune key NN hyperparameters, such as architecture, learning rate, and dropout rates. The model can detect small brain changes suggestive of PD because to this clever combination.The study includes both PD patients and healthy controls in its huge dataset of MRI images. The results show that our PSO-NN methodology outperforms traditional machine learning techniques and standalone NN models, exhibiting exceptional accuracy in PD diagnosis and a noteworthy. With the aid of MRI pictures, this research advances the creation of non-invasive, precise diagnostic tools for Parkinson's disease. The PSO-NN technique has the potential to help clinicians identify PD early, enable prompt therapies, and enhance the quality of life for those who are affected. For a thorough PD diagnosis, future study will concentrate on further validation, clinical integration, and exploration of additional imaging modalities.

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Published

04.11.2023

How to Cite

Phadtare, C. ., Aparna P., P. ., Preethi, P., Sahoo, A. K. ., & Choudhary, A. . (2023). A Hybrid Particle Swarm Optimization-Neural Network Approach for Parkinson’s Disease Diagnosis from MRI Images. International Journal of Intelligent Systems and Applications in Engineering, 12(3s), 421–430. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3723

Issue

Vol. 12 No. 3s (2024)

Section

Research Article

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Copyright (c) 2023 Chinmay Phadtare, Patange Aparna P., Preethi, Ashok Kumar Sahoo, Ankur Choudhary

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