Multi-Task Weakly-Supervised Attention Network for Dementia Status Estimation with Structural MRI
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PROJ20064
Description
Alzheimer’s disease (AD) is a permanent and dynamic psyche disease that consistently wrecks memory force and thinking capacity aptitudes that it impacts the ordinary everyday practice of person. Structural imaging based on magnetic resonance is an integral part of the clinical assessment of patients with suspected Alzheimer dementia. Prospective data on the natural history of change in structural markers from preclinical to overt stages of Alzheimer disease are radically changing how the disease is conceptualized, and will influence its future diagnosis and treatment. Atrophy of medial temporal structures is now considered to be a valid diagnostic marker at the mild cognitive impairment stage. Structural imaging is also included in diagnostic criteria for the most prevalent non-Alzheimer dementias, reflecting its value in differential diagnosis. In this we proposes a deep learning classification model to recognize the Alzheimer disease. The Convolutional Neural Network algorithm is implemented and classify the type of Alzheimer disease from structural MRI gray scale images and find the result in terms of accuracy.
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