Intelligent Control and Monitoring of Infectious Viral Diseases Based on Deep Learning

Every year, many deaths occur due to various strains of viral diseases, especially during the Arbaeen procession, numerous crowds in blessed places, and the beginning of the academic year of schools and universities in the country. The spread of compliance with health protocols was considered the best solution to stop this disease, and each individual's commitment to these protocols played a crucial role. Since manually monitoring compliance with health protocols is time-consuming, laborious, and error-prone, using an intelligent monitoring system to check people's mask coverage and identify symptomatic people regardless of quarantine regulations is strongly required in public environments. This article proposes an automatic hardware/software system based on artificial intelligence to identify people's mask cover and measure body temperature, which performs face recognition, mask cover detection and body temperature measurement, respectively, using the Viola Jones algorithm, convolutional neural network and temperature sensor. The Viola Jones converts a gray-level image to face area detected of the image. CNN model that optimized through transfer learning for classifying images is the ResNet type. If a person has not used a mask or the person’s body temperature is higher than 37.5 degrees, the system will issue a warning. The proposed model was obtained accuracies of 99% and 98% for the training and validation sets at Epoch 18. In the field evaluation, this system was able to achieve 96% accuracy in face recognition and 100% accuracy in mask cover detection. This smart system can be used to monitor compliance with health protocols in public centers.