Early Detection of Alzheimer’s Disease using Convolutional Neural Network Architecture
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Abstract
Alzheimer's disease is an extremely popular cause of dementia which leads to memory loss, problem-solving and other thinking abilities that are severe enough to interfere with daily life. Detection of Alzheimer’s at a prior stage is crucial as it can prevent significant damage to the patient’s brain. In this paper, a method to detect Alzheimer’s Disease from Brain MRI images is proposed. The proposed approach extracts shape features and texture of the Hippocampus region from the MRI scans and a Neural Network is used as a Multi-Class Classifier for detection of AD. The proposed approach is implemented and it gives better accuracy as compared to conventional approaches. In this paper, Convolutional Neural Network is the Neural Network approach used for the detection of AD at a prodromal stage.
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References
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