The smart detection of neuro-pathological effects of alzheimer patients using neural networks

Abstract

Neural networks have been increasingly utilized for smart detection of neuro-pathological effects of patients with Alzheimer's Disease. By leveraging the vast amounts of data available through imaging techniques such as magnetic resonance imaging (MRI), researchers have been able to develop deep learning models that accurately classify and diagnose Alzheimer's Disease. These neural networks are trained on the images of brain scans and other associated data as input. Once the algorithms have been trained, they can be used to detect subtle changes in the brain scans of Alzheimer's patients and can pinpoint certain pathology associated with the disease. This automated technique can aid in the early diagnosis and treatment of Alzheimer’s patients, thus improving patient outcomes.

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