Alzheimer’s disease effects human brain cells and results in dementia. The gradual deterioration of the brain cells results in disability of performing daily routine tasks. The treatment for this disease is still not mature enough. However, its early diagnosis may allow restraining the spread of disease. For early detection of Alzheimer’s through brain Magnetic Resonance Imaging (MRI), an automated detection and classification system needs to be developed that can detect and classify the subject having dementia. These systems also need not only to classify dementia patients but to also identify the four progressing stages of dementia. The proposed system works on an efficient technique of utilizing transfer learning to classify the images by fine-tuning a pre-trained convolutional network, AlexNet. The architecture is trained and tested over the pre-processed segmented (Grey Matter, White Matter, and Cerebral Spinal Fluid) and un-segmented images for both binary and multi-class classification. The performance of the proposed system is evaluated over Open Access Series of Imaging Studies (OASIS) dataset. The algorithm showed promising results by giving the best overall accuracy of 92.85% for multi-class classification of un-segmented images.

Alzheimer's Disease (AD) is the most common form of dementia. It gradually increases from mild stage to severe, affecting the ability to perform common daily tasks without assistance. It is a neurodegenerative illness, presently having no specified cure. Computer-Aided Diagnostic Systems have played an important role to help physicians to identify AD. However, the diagnosis of AD into its four stages; No Dementia, Very Mild Dementia, Mild Dementia, and Moderate Dementia remains an open research area. Deep learning assisted computer-aided solutions are proved to be more useful because of their high accuracy. However, the most common problem with deep learning architecture is that large training data is required. Furthermore, the samples should be evenly distributed among the classes to avoid the class imbalance problem. The publicly available dataset (OASIS) has serious class imbalance problem. In this research, we employed a transfer learning-based technique using data augmentation for 3D Magnetic Resonance Imaging (MRI) views from OASIS dataset. The accuracy of the proposed model utilizing a single view of the brain MRI is 98.41% while using 3D-views is 95.11%. The proposed system outperformed the existing techniques for Alzheimer disease stages.

Computer-assisted language learning (CALL) systems provide an automated framework to identify mispronunciation and give useful feedback. Traditionally, handcrafted acoustic-phonetic features are used to detect mispronunciation. From this line of research, this paper investigates the use of the deep convolutional neural network for mispronunciation detection of Arabic phonemes. We propose two methods with different techniques, i.e., convolutional neural network features (CNN_Features)-based technique and a transfer learning-based technique to detect mispronunciation detection. In the first method, we use deep CNN features to detect mispronunciation. We also extract features from different layers of CNN (layer4 to layer7) to train k-nearest neighbor (KNN), support vector machine (SVM), and neural network (NN) classifiers. In the transfer learning-based method, we trained the CNN using transfer learning to detect mispronunciation. To evaluate the performance of the system, we compare the results of these methods with baseline handcrafted features-based method for 28 Arabic phonemes. In the baseline method, we use the same classifiers; KNN, SVM, and NN to detect mispronunciation. The experimental results show that handcrafted_features method, CNN_features, and transfer learning-based method achieve an accuracy of 82%, 91.7%, and 92.2%, respectively. The performance analysis shows that transfer learning-based method outperforms handcrafted_features and transfer CNN_features-based methods and achieve an accuracy of 92.2%. The proposed transfer learning-based method also outperforms the state-of-art techniques in term of accuracy. INDEX TERMS Mispronunciation detection, deep convolutional neural network, computer-assisted language learning, and transfer learning.

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