Paper Key : IRJ************473
Author: R.sindhu,S.puvaneswari
Date Published: 15 Jul 2024
Abstract
Abstract Parkinsons disease (PD) is a progressive neurodegenerative condition characterized by the gradual deterioration of specific nerve cells in the brain, particularly those responsible for producing dopamine. Existing systems implemented to detect PD using different types of Machine Learning (ML) models such as Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), and K-Nearest Neighbour (KNN), to differentiate between healthy and PD patients by MRI. But these algorithms are time-consuming and accuracy is less. So this study introduces a novel approach for the early prediction of Parkinsons disease (PD) using Convolutional Neural Networks (CNN). Leveraging the power of deep learning, specifically tailored to image analysis, our proposed CNN algorithm analyses medical imaging data to detect subtle patterns indicative of Parkinson's disease. The research utilizes a diverse dataset of neuroimaging scans, incorporating magnetic resonance imaging (MRI), to train and validate the CNN model. The system aims to provide accurate and efficient predictions, enabling early diagnosis and intervention. Through rigorous evaluation and validation, our CNN algorithm demonstrates promising results, showcasing its potential as a valuable tool in disease diagnosis. This project contributes to the ongoing efforts in leveraging advanced technology for the early detection and management of neurodegenerative diseases, ultimately improving patient outcomes and enhancing the effectiveness of healthcare interventions.
DOI Requested