<p>Talking about gestures makes us return to the historical beginning of human communication, because, in fact, there is no language completely free of gestures. People cannot communicate without gestures. Any action or movement without gestures is free of real feelings and cannot express the thoughts. The purpose from any hand gestures recognition system is to recognizes the hand gesture and used it to transfer a certain meaning or for computer control or and device. Our paper introduced a low cost system to recognize the hand gesture in real-time. Generally, the system divided into five steps, one to image acquisition, second to pre-processing the image, third for detection and segmentation of hand region, four to features extraction and five to count the numbers of fingers and gestures recognition. The system has coded by Python language, PyAutoGUI library, OS Module of Python and the OpenCV library.</p>

Melanoma is a form of cancer that begins in melanocytes (cells that make the pigment melanin). It can affect the skin only, or it may spread to the organs and bones. It is less common, but more serious and aggressive than other types of skin cancer. Melanoma can be of benign or malignant. Malignant melanoma is the dangerous condition, while benign is not. In order to reduce the death rate due to malignant melanoma skin cancer, it is necessary to diagnose it at an early stage. In this paper, a detection system has been designed for diagnosing melanoma in early stages by using digital image processing techniques. The system consists of two phases: the first phase detects whether the pigmented skin lesion is malignant or benign; the second phase recognizes malignant melanoma skin cancer types. Both first and second phases have several stages. The experimental results are acceptable.

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<p>Alzheimer's disease (AD) is a brain disease that significantly declines a person's ability to remember and behave normally. By applying several approaches to distinguish between various stages of AD, neuroimaging data has been used to extract different patterns associated with various phases of AD. However, because the brain patterns of older adults and those in different phases are similar, researchers have had difficulty classifying them. In this paper, the 50-layer ResNet is modified by adding extra convolution layers to make the extracted features more diverse. Besides, the activation function (ReLU) was replaced with (Leaky ReLU) because ReLU takes the negative parts of its input, drops them to zero, and retains the positive parts. These negative inputs may contain useful feature information that could aid in the development of high-level discriminative features. Thus, Leaky ReLU was used instead of ReLU to prevent any potential loss of input information. In order to train the network from scratch without encountering the issue of overfitting, we added a dropout layer before the fully connected layer. The proposed method successfully classified the four stages of AD with an accuracy of 97.49 % and 98 % for precision, recall, and f1-score.</p>

Breast cancer is the most cause of death for women above age 40 around the world. In this paper, we propose a method to detect microcalcifications in digital mammography images using two-dimensional Discrete Wavelets Transform and image enhancement techniques for removing noise as well as to get a better contrast. The initial step is applying a preprocessing techniques to improve the edge of the breast and then segmentation process (Region of interest) for eliminating some regions in the image, which are not useful for the mammography interpretation. Then unsharp masking and histogram modification technique has used to enhance the contrast of the image and to clarify some details like microcalcifications. Lastly, Discrete Wavelets Transform applied for detecting the abnormality. The proposed method has evaluated using the Mammographic Image Analysis Society (AS) mammography databases. The proposed method has achieved acceptable results.