International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 09 Issue: 07 | July 2022
p-ISSN: 2395-0072
www.irjet.net
Parkinson’s Disease Detection Using Transfer Learning Dr. Hamsa S, B Murali mohan, P Ram Charan, T Abhinash Reddy, S Bramha Teja M. Tech (VLSI and Embedded Systems Design), Assistant Professor, B.E(Electronics and Communication), Jain university, Bangalore, India --------------------------------------------------------------------------***-------------------------------------------------------------------Abstract: Parkinson’s disease (PD) is an incurable neurological disorder disease. But there is no still no standard
medical provision to identify Parkinson’s disease. In this study, a fine motor symptom that is sketching has been studied. The experiments are done on a significant number of PD patients and healthy group (Without PD). We proposed a system that can determine the sketching and report whether a PD patient’s sketch or not. Deep learning algorithms can deal with the solution of different brain generalizing neural networks with the same design. Thus we applied Transfer Learning to classify sketched images to discriminate or identify Parkinson’s Disease (PD) affected patients from the regular healthy group. The experiment was done on different convolutional models with transfer learning method and applying on spiral and wave sketched data. By using Inception v3 and Resnet50 model with spiral sketching and wave sketching, the accuracy is more for the Inception v3. We have used the transfer learning which enhanced the model performance.
Keywords: Parkinson’s disease, Deep learning, Convolutional Neural Network, Transfer Learning I.
INTRODUCTION
In this era, more than seven million people worldwide are affected by Parkinson’s disease (PD), according to a recent study. Nowadays, this incurable disease is increasing tremendously. This disease gets its name from James Parkinson, who earlier described it as a paralysis agitans and later gave his surname was known as a PD. Parkinson’s disease causes a diverse set of symptoms ranging from tremor to cognitive impairment, hallucination, dementia, sleep disorders, etc. It is the most common neurodegenerative disease among aged people who are more than 50 years old. Till now, there is no complete cure. This paper aims the predict Parkinson’s disorder. To avoid the significant negative impact on PD patients, identification of PD in the premier stage is mandatory. Previous clinicians and researchers already used Handwriting and Spiral sketching to identify PD patients successfully in the premier stages. Spiral and wave sketching, and handwriting could be easily differentiated from healthy person (without PD) to a person affected by PD and the measurement of those sketching & handwriting are non-invasive. Parkinson’s disease symptoms are broadly divided into two groups. One is Motor symptoms another one is Non motor symptoms. Motor symptoms are tremor (involuntary movement of the legs/ hands), stiffness (difficulty in moving the parts of the body), slowness in daily activities, impaired balance, shuffling gait. On the other hand, non-motor symptoms are difficulties with memory, slowness of thought, anxiety and depression, insomnia and fatigue, vision problem, hallucinations and delusions, speech and swallowing problems.
II.
Methodology
Data Collection: We collected data from Kaggle’s dataset. This dataset is a set of spiral and wave sketches from 55 subjects where 28 subjects from the healthy control group (without PD) and 27 subjects from the Parkinson’s group. The dataset contains 102 spiral sketching images and 102 wave sketching images. A tablet, A3 size paper, and a pen were used to record sketching. Sample Images of the sketches for Healthy (without PD) and Parkinson Group.
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