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. The attained validation set accuracies are as high as 99.12\% for the Inception version 3 network and 97.97\% for the Xception network. The presented results suggest that meaningful predictors composed of sMRI and network measures may offer the possibility for early detection of subjects in the early stages of AD .
Alzheimer 's 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 and Xception; as well as custom Convolutional Neural Network (CNN) built with the help of separable convolutional layers which can automatically learn the generic features from imaging data for classification. Our study is conducted using cross-sectional T1-weighted structural MRI brain images from Open Access Series of Imaging Studies (OASIS) database to maintain the size and contrast over different MRI scans. Experimental results show that the transfer learning approaches exceed the performance of non-transfer learning based approaches demonstrating the effectiveness of these approaches for the binary AD classification task .
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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
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;
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in the Keras deep learning library. The attained validation set accuracies are as high as 99.12\% for the Inception version 3 network and 97.97\% for
and Xception; as well as custom Convolutional Neural Network (CNN) built with the help of separable convolutional layers which can automatically learn the generic features from imaging data for classification. Our study is conducted using cross-sectional T1-weighted structural MRI brain images from Open Access Series of Imaging Studies (OASIS) database to maintain the size and contrast over different MRI scans. Experimental results show that
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Xception network. The presented results suggest that meaningful predictors composed of sMRI and network measures may offer the possibility for early detection of subjects in the early stages of AD
transfer learning approaches exceed the performance of non-transfer learning based approaches demonstrating the effectiveness of these approaches for the binary AD classification task