Stages of Progression Classification of Alzheimer’s Disease Using Deep Transfer Learning Models with Over-sampling

Abstract

Alzheimer's disease is a chronic neurodegenerative disease that affected patients loss of memory, ability of thinking, reading, and cognitive decline. The Alzheimer's disease divided stages of progression into four general stages on the basis of their symptoms. The early diagnosis helps to slow down the disease and reduce the costs of treatment. Since Alzheimer's disease has four stages of progression, the classification problems are those where a stage must be predicted in the case of an unequal number of instances of each class. This study has proposed the classification the stages of progression of Alzheimer's disease using four deep transfer learning models such as VGG19, Xception, ResNet50, and MobileNetV2. The proposed models enhance to classify Alzheimer's disease into four stage of progression. The four transfer learning of CNN architectures were applied to train, test, and validate based on the Alzheimer image dataset. The models achieved an accuracy level of VGG19, Xception, ResNet50, and MobileNetV2 model of 77.73%, 82.46%, 76.28% and 79.29%, respectively. By considering the F1 score, the Xception, VGG19, and ResNet50, and MobileNetV2 models gave the high score of 0.7995, 0.8870, 0.8305, and 0.5993, respectively. Therefore, the VGG19 model is the best model by considering the F1 score that means the VGG19 model is the best model in overall performance. Finally, this study measures the AUC value that indicates the ability to classify between classes. The results show that AUC value of MobileNetV2, Xception, ResNet50, and VGG19 are 0.9290, 0.9539, 0.7937, and 0.8037, respectively. Therefore, the Xception model is the best model that has capable of distinguishing the stages of progression of the Alzheimer's disease.