Applying convolutional neural networks for pre-detection of alzheimer's disease from structural MRI data

Abstract

During the recent years, there have been many studies implemented on the automatic diagnosis of Alzheimer's Disease (AD) using different methods. The focus of most of these studies has relied upon the detection of AD from neu-roimaging data. However, recognizing symptoms early as much as possible(Pre-detection) is crucial as disease modifying drugs will be most effective if administered early in the course of disease, before the occurrence of irreversible brain damages. Therefore, there is a high importance of utilizing automated techniques for the purpose of pre-detection of AD symptoms from such data. We report an experimental approach to evaluate the best pre-detection method of AD. Our study consists of two main experiments. Those two experiments were implemented using the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Prior to our first experiment we have stated an assumption which is, if there is a successful AD detection method that will be successful in AD pre-detection also. Different studies have used different data sets and different diagnosing methods. Therefore we have verified the existing and the most successful detection method which is Support Vector Machine (SVMs) as the first experiment. According to the results obtained from the initial experiment (detection study) the sensitivity is 95.3%, the specificity is 71.4% and the accuracy is 84.4% with use of a SVM. Since those results were not successful, a deep learning based technique (Convolutional Neural Network) was proposed as the second experiment. The proposed Convolutional Neural Network (CNN) model was being tested using different image segmentation methods and different datasets. Finally the best image segmentation method obtained a high accuracy around 96% (sensitivity — 96%, specificity — 98%). And the CNN model remains unbiased to the dataset. Results of those experiments suggest an important role for early diagnosis of Alzheimer's disease using image processing and deep learning techniques.