This paper focused on the problem of diagnosis of Alzheimer’s disease via the combination of deep learning and radiomics methods. We proposed a classification model for Alzheimer’s disease diagnosis based on improved convolution neural network models and image fusion method and compared it with existing network models. We collected 182 patients in the ADNI and PPMI database to classify Alzheimer’s disease, and reached 0.906 AUC in training with single modality images, and 0.941 AUC in training with fusion images. This proved the proposed method has better performance in the fusion images. The research may promote the application of multimodal images in the diagnosis of Alzheimer’s disease. Fusion images dataset based on multi-modality images has higher diagnosis accuracy than single modality images dataset. Deep learning methods and radiomics significantly improve the diagnosing accuracy of Alzheimer’s disease diagnosis.
Convolutional Autoencoder based Deep Learning Approach for Alzheimer's Disease Diagnosis using Brain MRI
