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Go to Editorial ManagerDeep learning modeling could provide to detected Corona Virus 2019 (COVID-19) which is a critical task these days to make a treatment decision according to the diagnostic results. On the other hand, advances in the areas of artificial intelligence, machine learning, deep learning, and medical imaging techniques allow demonstrating impressive performance, especially in problems of detection, classification, and segmentation. These innovations enabled physicians to see the human body with high accuracy, which led to an increase in the accuracy of diagnosis and non-surgical examination of patients. There are many imaging models used to detect COVID-19, but we use computerized tomography (CT) because is commonly used. Moreover, we use for detection a deep learning model based on convolutional neural network (CNN) for COVID-19 detection. The dataset has been used is 544 slice of CT scan which is not sufficient for high accuracy, but we can say that it is acceptable because of the few datasets available in these days. The proposed model achieves validation and test accuracy 84.4% and 90.09%, respectively. The proposed model has been compared with other models to prove superiority of our model over the other models.
Continuous Positive Airway Pressure (CPAP) ventilation remains a mainstay treatment for different respiratory disorders. Good pressure stability and pressure reduction during exhalation are of major importance condition to ensure the clinical efficacy and comfort of CPAP therapy. Obstructive Sleep Apnea (OSA) and today coronavirus (COVID-19) are the main two diseases mitigated by the CPAP. This paper introduced a systematic review of the CPAP design in terms of the hardware design, Simulation-based CPAP system, control algorithm, and the measured performance. The accuracy is used as measurement of performance and calculated from the pressure value. The accuracy was compared to the predefined U.S. Food and Drug Administration (FDA)-based threshold value in which it considers this value as a reference. The results related to the modern CPAP devices introduced in this study to explain the accuracy of experimental CPAP. These were compared with a commercial CPAP devices. Also, it was revealed how the results coincide with the error ratio defined by the FDA as an evaluation measurement. The FDA error ratio determines the performance of the optimized CPAP device. This work is the first review that presented the knowledge about engineering design of the CPAP system, so it will be the first in the literature.