Recently, the recognition of human hand gestures is becoming a valuable technology for various applications like sign language recognition, virtual games and robotics control, video surveillance, and home automation. Owing to the recent development of deep learning and its excellent performance, deep learning-based hand gesture recognition systems can provide promising results. However, accurate recognition of hand gestures remains a substantial challenge that faces most of the recently existing recognition systems. In this paper, convolutional neural networks (CNN) framework with multiple layers for accurate, effective, and less complex human hand gesture recognition has been proposed. Since the images of the infrared hand gestures can provide accurate gesture information through the low illumination environment, the proposed system is tested and evaluated on a database of hand-based near-infrared which including ten gesture poses. Extensive experiments prove that the proposed system provides excellent results of accuracy, precision, sensitivity (recall), and F1-score. Furthermore, a comparison with recently existing systems is reported.

Pneumonia represents a life-endangering and deadly disease that results from a viral or bacterial infection in the human lungs. The earlier pneumonia’s diagnosing is an essential aspect in the processes of successful treatment. Recently, the developed methods of deep learning that include several layers of processing to comprehend the stratified data representation have obtained the best results in various domains, especially in the identification and classification of human diseases. Therefore, for improving the systems’ performance for detecting pneumonia disease, there is a requirement for implementing automatic models based on deep learning models that have the ability to diagnose the images of chest X-rays and to facilitate the detection process of pneumonia novices and experts. A convolutional neural network (CNN) model is developed in this paper for detecting pneumonia via utilizing the images of chest X-rays. The proposed framework encompasses two main stages: the stage of image preprocessing and the stage of extracting features and image classification. The proposed CNN model provides high results of precision, recall, F1-score, and accuracy by 98%, 98%, 97%, and 99.82%, respectively. Regarding the obtained results, the proposed CNN model-based pneumonia detection has achieved a better result of consistency and accuracy, and it has outperformed the other pretrained deep learning models such as residual networks (ResNet 50) and VGG16. Furthermore, it exceeds the recently existing models presented in the literature. Thus, the significant performance of the proposed CNN model-based pneumonia detection in all measures of performance can provide effective services of patient care and decrease the rates of mortality.

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