One of the deadliest pandemics is now happening in the current world due to COVID-19. This contagious virus is spreading like wildfire around the whole world. To minimize the spreading of this virus, World Health Organization (WHO) has made protocols mandatory for wearing face masks and maintaining 6 feet physical distance. In this paper, we have developed a system that can detect the proper maintenance of that distance and people are properly using masks or not. We have used the customized attention-inceptionv3 model in this system for the identification of those two components. We have used two different datasets along with 10,800 images including both with and without Face Mask images. The training accuracy has been achieved 98% and validation accuracy 99.5%. The system can conduct a precision value of around 98.2% and the frame rate per second (FPS) was 25.0. So, with this system, we can identify high-risk areas with the highest possibility of the virus spreading zone. This may help authorities to take necessary steps to locate those risky areas and alert the local people to ensure proper precautions in no time.

Brain MRI (Magnetic Resonance Imaging) classification is one of the most significant areas of medical imaging. Among different types of procedures, MRI is the most trusted one to detect brain diseases. Manual and semi-automated segmentations need highly experienced radiologists and much time to detect the problem. Recently, deep learning methods have taken attention due to their automation and self-learning techniques. To get a faster result, we have used different algorithms of Convolutional Neural Network (CNN) with the help of transfer learning for classification to detect diseases. This procedure is fully automated, needs less involvement of highly experienced radiologists, and does not take much time to provide the result. We have implemented six deep learning algorithms, which are InceptionV3, ResNet152V2, MobileNetV2, Resnet50, EfficientNetB0, and DenseNet201 on two brain tumor datasets (both individually and manually combined) and one Alzheimer's dataset. Our first brain tumor dataset (total of 7,023 imagestraining 5,712, testing 1,311) has 99-100 percent training accuracy and 98-99 percent testing accuracy. Our second tumor dataset (total of 3,264 images-training 2,870, testing 394) has 100 percent training accuracy and 69-81 percent testing accuracy. The combined dataset (total of 10,000 images-training 8,000, testing 2,000) has 99-100 percent training accuracy and 98-99 percent testing accuracy. Alzheimer's dataset (total of 6,400 images-training 5,121, testing 1,279, 4 classes of images) has 99-100 percent training accuracy and 71-78 percent testing accuracy. CNN models are renowned for showing the best accuracy in a limited dataset, which we have observed in our models.