Double-Enhanced Convolutional Neural Network for Multi-Stage Classification of Alzheimer’s Disease

Being known as an irreversible neurodegenerative disease which has no cure to date, detection and classification of Alzheimer's disease (AD) in its early stages is significant so that the deterioration process can be slowed down.Generally, AD can be classified into three major stages, ranging from the "normal control" stage with no symptoms shown, the "mild cognitive impairment (MCI)" stage with minor symptoms, and the AD stage which depicts major and serious symptoms.Due to its generative features, MCI patients tend to easily progress to the AD stage if appropriate diagnosis and prevention measures are not taken.However, it is difficult to accurately identify and diagnose the MCI stage due to its mild and insignificant symptoms that often lead to misdiagnosis.In other words, the classification of multiple stages of AD has been a challenge for medical professionals.Thus, deep learning models like convolutional neural networks (CNN) have been popularly utilized to overcome this challenge.Nevertheless, they are still limited by the issue of limited medical images and their weak feature representation ability.In this study, a doubleenhanced CNN model is proposed by incorporating an attention module and a generative adversarial network (GAN) to classify magnetic resonance imaging (MRI) brain images into 3 classes of AD.MRI images are obtained from the Open Access Series of Imaging Studies (OASIS) database and four experiments are done in this study to observe the classification performance of the enhanced model.From the results obtained, it can be observed that the enhanced CNN model with GAN and attention module has achieved the best performance of 99% as compared to the other models.Hence, this study has shown that the double-enhanced CNN model has effectively boosted the performance of the deep learning model and overcame the challenge in the multi-stage classification of AD.