VGG-C Transform Model with Batch Normalization to Predict Alzheimer's Disease through MRI Dataset

Abstract

Alzheimer's disease is the most common cause of dementia and is a generic term for memory and other cognitive abilities that are severe enough to interfere with daily life. In this paper, we propose an improved prediction method for Alzheimer's disease using a quantization method that transforms the MRI data set using a VGG-C Transform model and a convolutional neural network (CNN) consisting of batch normalization. MRI image data of Alzheimer's disease are not fully disclosed to general research because it is data from real patients. So, we had to find a solution that could maximize the core functionality in a limited image. In other words, since it is necessary to adjust the interval, which is an important feature of MRI color information, rather than expressing the brain shape, the brain texture dataset was modified in the quantized pixel intensity method. We also use the VGG family, where the VGG-C Transform model with bundle normalization added to the VGG-C model performed the best with a test accuracy of about 0.9800. However, since MRI images are 208 x 176 pixels, conversion to 224 x 224 pixels may result in distortion and loss of pixel information. To address this, the proposed VGG model-based architecture can be trained while maintaining the original MRI size. As a result, we were able to obtain a prediction accuracy of 98% and the AUC score increased by up to 1.19%, compared to the normal MRI image data set. It is expected that our study will be helpful in predicting Alzheimer's disease using the MRI dataset.