Alzheimer’s Diseases Detection By Using MRI Brain Images: A Survey:

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Zahraa Sh. Aaraji , Hawraa H. Abbas , Ameer Asady


Alzheimer's disease (AD) is one of the most common public health issues the world is facing today. This disease has a high prevalence primarily in the elderly accompanying memory loss and cognitive decline. At present, there is no specific treatment for this disease. Early and accurate diagnosis of AD become a challenging task which many authors have developed numerous computerized automatic diagnosis systems utilizing neuroimaging and other clinical data. These studies have identified the importance of structural differences in brain regions such as the entorhinal cortex, hippocampi, and other brain areas between Alzheimer-affected brain and a healthy brain. Magnetic Resonance Imaging (MRI) scanners have proven the potentiality to study AD-related brain structural variations, consequently, structural MR imaging techniques have been exploited as a significant diagnostic tool when reporting a cognitive decline. The researchers showed promising results not only for excluding non-neurodegeneration causes, but rather to accurately identify AD neurodegenerations. Machine Learning (ML) and subfield deep learning (DL) has become prominent techniques for detecting AD at their early stages. Here, brief literature of the previously adopted AD diagnosis techniques will be reviewed, including traditional diagnosis methods, and advancing to the relevant modern employment of DL in AD diagnosis.

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