Before revision: Alzheimer Disease (AD) is the most common form of dementia affecting the elderly population worldwide. Many neuroimaging modalities have been used to check the detection and progression of AD of which structural Magnetic Resonance Imaging (sMRI) is an important one. The recent rise in the popularity of deep learning methods with applications in computer vision, reinforcement learning and artificial intelligence has created a resurgence in the application of these methods to the classification of AD through different imaging modalities. In this study, by utilizing the concept of transfer learning in deep learning, we propose a classification framework to differentiate subjects with Clinical Dementia Rating (CDR) of zero from subjects with CDR greater than zero by using deep learning architecturessuch as Xception and Inception version 3 in the Keras deep learning library.
After revision: Alzheimer Disease (AD) is an irreversible devastative neurodegenerative disorder associated with progressive impairment of memory and cognitive functions. Its early diagnosis is crucial for the development of possible future treatment option(s). Structural magnetic resonance images (sMRI) plays an important role to help in understanding the anatomical changes related to AD especially in its early stages. Conventional methods require the expertise of domain experts and extract hand-picked features such as gray matter substructures and train a classifier to distinguish AD subjects from healthy subjects. Different from these methods, this paper proposes to construct multiple deep 2D convolutional neural networks (2D-CNNs) to learn the various features from local brain images which are combined to make the final classification for AD diagnosis. The whole brain image was passed through two transfer learning architectures; Inception version 3 in the Keras deep learning library.