This project investigates the utilization of machine learning techniques for analyzing neuroimaging (MRI) data to differentiate patients diagnosed with Focal Cortical Dysplasia Type II (FCD Type II). FCD Type II presents challenges in accurate diagnosis, prompting the exploration of alternative approaches. The study involves preprocessing MRI data, extracting relevant features, and training machine learning models for classification. Performance evaluation metrics are employed to assess the models' efficacy in distinguishing patients with FCD Type II from healthy individuals or those with other neurological conditions. The research aims to contribute to improved diagnosis and management of FCD Type II through the integration of machine learning analysis into neuroimaging practices.

• This report represents the work of one or more WPI undergraduate students submitted to the faculty as evidence of completion of a degree requirement. WPI routinely publishes these reports on its website without editorial or peer review.

Creator

• Golden, Jonathan
• Kandasamy, Vivek

Publisher

• Worcester Polytechnic Institute

Identifier

• 121684
• E-project-042524-113645

Parola chiave

• Deep Learning
• MRI
• Machine Learning
• Python

Advisor

• Korkin, Dmitry
• Rodriguez, Angela C Incollingo
• Nephew, Benjamin

Year

• 2024

Date created

• 2024-04-25

Resource type

• Major Qualifying Project

Major

• Computer Science
• Bioinformatics & Computational Biology

Source

• E-project-042524-113645

Rights statement

• In Copyright