3-D Image Based Deep Learning for Dementia Diagnosis
DOI:
https://doi.org/10.47611/jsrhs.v12i3.4569Keywords:
Dementia, MRI, Diagnosis, Machine Learning, Neural Network, ConvolutionalAbstract
Dementia is a neurodegenerative disorder that greatly affects memory, thinking, and reasoning, impacting millions of people worldwide. Although dementia diagnosis is challenging and time-consuming, recent studies have shown promising results in using deep learning for dementia diagnosis by analyzing MRI scans. However, these studies are limited by access to data and the depth of analysis. In this study, we developed a deep learning model that utilizes the T1-weighted MRI scans from the Open Access Series of Imaging Studies (OASIS-3) dataset, which contains data from 1378 patients with varying degrees of cognitive decline. The model classified MRI scans into two categories, negative or positive diagnosis of dementia, based on the patients' clinical dementia rating (CDR). A 3-D convolutional neural network (CNN) was constructed with the TensorFlow framework and achieved an accuracy of 76.25%. This study demonstrates the potential that deep learning models have in the future of dementia diagnosis.
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Copyright (c) 2023 George Liu; Guillermo Goldsztein
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