Classification of Dementia Levels by Using Different Convolutional Neural Network Architectures

Abstract

Dementia or Alzheimer is a disease that causes symptoms such as forgetfulness and loss of physical ability, which will add to the individual's life in later stages, along with morphological changes in the brain. Unfortunately, a definitive treatment for these diseases has not yet been found. However, it is aimed at slowing down the progression of the disease to ensure that the patient is less affected by these adverse conditions and to protect living standards with early diagnosis of the disease. In addition, a complete diagnosis of the disease requires a series of tests and a tiring diagnostic phase to be evaluated by an experienced specialist. High-resolution magnetic resonance imaging is used to make this determination. This study tries to determine the stage of the disease or whether the individual is healthy by using MR.MR images of individuals in 4 stages of the disease, one of which is a healthy individual, were described as a classification problem and tried to be solved using VGG, Resnet, and Mobilenet architectures. Over 95% success has been achieved by supporting the proposed architecture with feature analysis and classical architectures.

Keywords

• Alzheimer
• dementia levels
• CNN
• SMOTE
• classification.

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