Welcome to my GitHub repository! Here, I present my MSc Clinical Neuroscience Thesis project, which focuses on creating a sequential Convolutional Neural Network (CNN) capable of classifying different cases of Frontotemporal Dementia (FTD) based on T1-weighted MRI data. If you're interested in the field of medical image analysis, deep learning, or neurodegenerative diseases, this repository provides insights into the development of a powerful tool for FTD diagnosis. This project is my first exposure to deep learning, coming from a background of Biomedical Science.
This repository showcases the work I conducted during my MSc Thesis project, which involved building a sequential Convolutional Neural Network (CNN) to classify different cases of Frontotemporal Dementia (FTD) using T1-weighted MRI data. FTD is a challenging neurodegenerative disease, and accurate early diagnosis is crucial for effective treatment and patient care.
The data used in this project was generously provided by the Frontotemporal Lobar Degeneration Neuroimaging Initiative (FTLDNI), a study funded by the National Institute on Aging and the National Institute on Neurological Disorders and Stroke. It's important to note that this data is completely open and free to use, with no ethical concerns. You can learn more about the initiative here.
The T1-weighted MRI data from FTLDNI is a valuable resource for research in neurodegenerative diseases. It contains imaging data from individuals with varying degrees of FTD, enabling the development of accurate classification models. The ethical and open nature of the data allows for collaborative research and advancements in the field.
In this project, I leveraged deep learning techniques to develop a sequential CNN model capable of classifying FTD cases from T1-weighted MRI scans. The model's architecture, training process, and evaluation metrics are documented in this repository. You can explore the Jupyter notebooks and code to gain insights into the methodology and results of this classification task.
Thank you for exploring the Frontotemporal Dementia Classification project! I hope that this work contributes to the advancement of FTD diagnosis and treatment. If you have any questions, issues, or suggestions, please feel free to open an issue.