A longitudinal spontaneous speech (machine learning audio) dataset for dementia diagnosis.
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Updated
Aug 15, 2022
A longitudinal spontaneous speech (machine learning audio) dataset for dementia diagnosis.
Brain white matter hyperintensity segmentation, with T1 and FLAIR MRI images, using UNet.
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
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)
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
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.
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
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