Alzheimer's Disease Classification from MRI Using Deep Learning

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Thongchai Photsathian
Thitiporn Suttikul
Worapong Tangsrirat

Abstract

Thailand has entered an aging society as a result of its population's longevity; in 2020, there were 11.627 million persons in Thailand who were 60 or older, or 17.57 percent of the country's overall population. By 2030, Thailand will have a population that is 60 years of age or older, making up approximately 28% of the total population. Everyone's body naturally deteriorates with age, and dementia, particularly Alzheimer's disease, is one of the more common conditions. The number of individuals who have this disease increases with age, increasing by around double every five years. Both treatment and prevention are not possible for this illness. Early detection of Alzheimer's disease increases the likelihood that symptoms can be treated to improve or delay further decline. In this study, simple data preparation techniques and magnetic resonance imaging (MRI) data were used to categorize Alzheimer's disease using deep neural network (DNN). The 5,121 total images used in this study were composed of 2,560 MRI images for the normal case and 2,561 MRI images for the Alzheimer case. According to the data analysis, this model has an accuracy of 97.56%, a precision of 98.22%, a recall of 96.89%, and an F1Score of 97.54%.

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