Brain white matter hyperintensity segmentation, with T1 and FLAIR MRI images, using UNet.

A longitudinal spontaneous speech (machine learning audio) dataset for dementia diagnosis.

Code for NeuraHealth: An Automated Screening Pipeline to Detect Undiagnosed Cognitive Impairment in Electronic Health Records using Deep Learning and Natural Language Processing

a low-cost, convenient, remote, and consistent platform for research into Dementia

Better dementia diagnosis through data

Msc. Thesis: Revising the clinical criteria for Dementia using explainable machine learning.

Final project for AC209A: Data Science 1 @ Harvard University. This project aims to understand how different factors affect whether a person develops dementia by utilizing various ML models to predict diagnosis outcomes.

To provide a complete pipeline to develop a deep learning model. More specifically, deep learning for computer vision (2D). Using a multiclass image classification (single label) deep learning model that can predict what stage (refer below) of Alzheimer's a patient is, from their MRI image. This study will focus on CNN Verus DCGAN (Generative)

This repository is includes the code used in the paper: TraMiner: Vision-based Analysis of Locomotion Traces for Cognitive Assessment in Smart-homes

Alzheimer's Disease Classification Research performed at University of Cagliari in 2020.

UK dementia diagnoses rise 40% in five years

Project to examine dataset of dementia patients and predict likelihood of dementia in future patients

To provide a complete pipeline to develop a deep learning model. More specifically, a multiclass classification (single label) deep learning model that can predict what stage of Alzheimer's a patient is, from their MRI image