Parkinson’s Disease Detection Using Deep Learning Techniques

Project Code :TCMAPY957

Objective

The main objective of Parkinson's Disease detection using deep learning techniques is to develop an accurate and reliable system that can analyze relevant data, such as voice or movement patterns, and classify individuals as either Parkinson's disease patients or healthy controls, thereby aiding in early diagnosis and treatment of the disease. This objective aims to leverage the power of deep learning algorithms to create a non-invasive and efficient tool for Parkinson's disease detection, enabling timely interventions and improved patient outcomes.

Abstract

Parkinson ’s disease (PD) is considered a malison for mankind for several decades. Its detection with the help of an automated system is a subject undergoing intense study. This entails a need for incorporating a Deep learning model for the early detection of PD. For discovering a full proof model, the cardinal prerequisite is to study the existing computational intelligent techniques in the field of research used for PD detection. Many existing models focus on singular modality or have a cursory analysis of multiple modalities. This encouraged us to provide a comparative literature study of four main modalities signifying major symptoms used for early detection of PD, namely, tremor at rest, bradykinesia, rigidity, and, voice impairment. State- of-the-art Mobile net is implemented.

Keywords: Parkinson’s Disease, Deep Learning. 

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.

Block Diagram

Specifications

SOFTWARE FRONT END REQUIREMENTS

H/W CONFIGURATION:

Processor- I3/Intel Processor

Hard Disk- 160GB

Key Board- Standard Windows Keyboard

Mouse- Two or Three Button Mouse

Monitor - SVGA

RAM- 8GB

S/W CONFIGURATION:


Operating System:  Windows 7/8/10

Server side Script:  HTML, CSS, Bootstrap & JS

Programming Language:  Python

Libraries:  Flask, Pandas, Mysql.connector, Os, Smtplib, Numpy

IDE/Workbench:  PyCharm

Technology:  Python 3.6+

Server Deployment:  Xampp Server


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