Paper PublishingParkinson's Disease Classification Using Machine Learning Techniques.
Objective
The objective of this project is to classifying the nervous disorder disease like Parkinson using Machine Learning Techniques like Random forest network.
Abstract
Parkinson's disease is a brain disorder that leads to shaking, stiffness, and difficulty with walking, balance, and coordination. In the early stages of Parkinson's disease, your face may show little or no expression. Your arms may not swing when you walk. Your speech may become soft or slurred. Parkinson's disease symptoms worsen as your condition progresses over time.
Here, in this project, we will classify whether the input image is a Parkinson/Healthy using one of the machine learning algorithms called Random Forest Classifier with the extraction of HOG (Histogram of Oriented Gradients) features. Kaggle Parkinson Dataset is used in our model for testing and training purpose.
Keywords: Parkinson, Histogram of Oriented Gradients (HOG), Random Forest Classifier.
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: Matlab 2018a or above
Hardware:
Operating Systems:
- Windows 10
- Windows 7 Service Pack 1
- Windows Server 2019
- Windows Server 2016
Processors:
Minimum: Any Intel or AMD x86-64 processor
Recommended: Any Intel or AMD x86-64 processor with four logical cores and AVX2 instruction set support
Disk:
Minimum: 2.9 GB of HDD space for MATLAB only, 5-8 GB for a typical installation
Recommended: An SSD is recommended A full installation of all Math Works products may take up to 29 GB of disk space
RAM:
Minimum: 4 GB
Recommended: 8 GB
Learning Outcomes
- Introduction to Matlab
- What is EISPACK & LINPACK
- How to start with MATLAB
- About Matlab language
- Matlab coding skills
- About tools & libraries
- Application Program Interface in Matlab
- About Matlab desktop
- How to use Matlab editor to create M-Files
- Features of Matlab
- Basics on Matlab
- What is an Image/pixel?
- About image formats
- Introduction to Image Processing
- How digital image is formed
- Importing the image via image acquisition tools
- Analyzing and manipulation of image.
- Phases of image processing:
- Acquisition
- Image enhancement
- Image restoration
- Color image processing
- Image compression
- Morphological processing
- Segmentation etc.,
- About Artificial Intelligence (AI)
- About Machine Learning
- About layers in AI (input, hidden and output layers)
- Building AI (ANN/CNN) architecture using Matlab
- We will be able to know what’s the term “Training” means in Artificial Intelligence
- About requirements that can influence the AI training process:
- Data
- Training data
- Validation data
- Testing data
- Hardware requirements to train network
- How to segment the object using AI
- How to extend our work to another real time applications
- Project development Skills
- Problem analyzing skills
- Problem-solving skills
- Creativity and imaginary skills
- Programming skills
- Deployment
- Testing skills
- Debugging skills
- Project presentation skills
- Thesis writing skills
Demo Video
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Disclaimer - Takeoff Edu Group Projects, are not associated or affiliated with IEEE, in any way. The IEEE Projects mentioned here are mentioned in the context of student projects, whose ideas are derived from IEEE publications, and not projects of or by IEEE.
