Technologies Used
Python
Machine Learning
Medical Imaging
CNN
SVM
Random Forest
MRI Analysis
Project Overview
Compared 3D CNNs, SVMs, and Random Forests for classifying MRI scans into PD, Control, SWEDD, and Prodromal groups. Random Forest achieved the highest accuracy (65.9%) for multiclass classification, while SVM performed best (76.4%) for binary classification.
Key Features
- Implemented using Python, Machine Learning, Medical Imaging
- Developed during May 2023 - Jul 2023
- Focused on performance, usability, and modern design principles
- Utilized best practices for code organization and maintainability
Development Process
This project was developed with a focus on creating a robust and scalable solution. The development process involved careful planning, implementation of key features, rigorous testing, and deployment.
Project Details
Project Type
Python
Timeline
May 2023 - Jul 2023
Primary Technologies
Python
Machine Learning
Medical Imaging
Related Skills
PythonMachine LearningMedical ImagingCNNSVMRandom Forest