Alzheimer's disease (AD) remains a major public health problem. This neurodegenerative pathology affects generally old people. Its symptoms are loss of memory followed over the years by more hard ability of expression and various handicaps. Therefore, early detection of AD is become an active research area in recent years. In this paper, we propose a deep based method for the detection of AD (i.e. classify brain images into normal brain or brain with AD). The proposed method contains two main steps. The first step is region of interest extraction; it is based on the partition of the image into separate blocks to extract only the part that contains the hippocampus of the brain. The second step is the classification of images using two deep based techniques namely convolutional neural network (CNN) and Transfer Learning. In one hand, CNN allows extracting the characteristics from brain images, then classifies them into normal brain or AD brain. Transfer Learning, in the other hand, consists of using features acquired from the Alexnet architecture to classify the images. We have assessed the proposed method on Oasis dataset (Open Access Series of Imaging Studies). The obtained results show that the classification of images using Transfer Learning with 92.86 % outperformed the CNN's classification rate.

The coronavirus disease (COVID-19) is an unparalleled crisis leading to a huge number of casualties and security problems. In order to reduce the spread of coronavirus, people often wear masks to protect themselves. This makes face recognition a very difficult task since certain parts of the face are hidden. A primary focus of researchers during the ongoing coronavirus pandemic is to come up with suggestions to handle this problem through rapid and efficient solutions. In this paper, we propose a reliable method based on occlusion removal and deep learning-based features in order to address the problem of the masked face recognition process. The first step is to remove the masked face region. Next, we apply three pre-trained deep Convolutional Neural Networks (CNN) namely, VGG-16, AlexNet, and ResNet-50, and use them to extract deep features from the obtained regions (mostly eyes and forehead regions). The Bag-of-features paradigm is then applied to the feature maps of the last convolutional layer in order to quantize them and to get a slight representation comparing to the fully connected layer of classical CNN. Finally, Multilayer Perceptron (MLP) is applied for the classification process. Experimental results on Real-World-Masked-Face-Dataset show high recognition performance compared to other state-of-the-art methods.