A novel automated deep learning approach for Alzheimer's disease classification

Abstract

Alzheimer's disease is a degenerative brain illness, incurable and progressive. Globally for every two seconds, someone is affected by Alzheimer's disease. Alzheimer's disease in the elderly is difficult to diagnose due to the complexity of the brain structure. Its pixel intensity is similar and systematic distinction is necessary. Deep learning has inspired a lot of interest in recent years in tackling challenges in a variety of fields, including medical imaging. One of the drawbacks of deep learning approach is the inability to detect changes in functional connectivity in mild cognitive impairment (MCI) patients' functional brain networks. In this paper, we utilize deep features extracted from two pre-trained deep learning models to tackle this issue. The proposed models DenseNet121 and MobileNetV2 is used to perform the task of Alzheimer's disease multi-class classification. In this method, initially we increased 70 % of dataset and generated images by using cycle generative adversarial networks (CycleGAN). We achieved 98.82% of accuracy with proposed models. It gives best results compared to existing models.