Communication between hearing impaired and general people is one of the most immense problem nowadays. The main medium of conversation with deaf people is through sign language. But it is an arduous task to learn and communicate with the sign language for any class of people. Over the years, researchers from diverse backgrounds tried to establish a model to automate the process of the detection of sign language. Traditional machine learning technique — k Nearest Neighbor (kNN), Support Vector Machine (SVM) etc. and Neural Network architectures—Back-propagation method, Convolutional Neural Network (CNN), Ensemble Neural Network etc. are existing methods by which the researchers proposed their work. In this paper, we worked on the detection of Bangla sign digits. We adopted and modified Capsule Network by the means of detecting Bangla sign digits. In a capsule network architecture, a capsule is a group of neurons that constitutes the feature vector and spatial properties of the object. A seven-layer of capsule network architecture along with some pre-processing is proposed for the detection. The Ishara-Lipi dataset is used for the training of the model and we get 98.84% accuracy for the classification.

The pandemic, originated by novel coronavirus 2019 (COVID-19), continuing its devastating effect on the health, well-being, and economy of the global population. A critical step to restrain this pandemic is the early detection of COVID-19 in the human body, to constraint the exposure and control the spread of the virus. Chest X-Rays are one of the non-invasive tools to detect this disease as the manual PCR diagnosis process is quite tedious and time-consuming. In this work, we propose an automated COVID-19 classifier, utilizing available COVID and non-COVID X-Ray datasets, along with High Resolution Network (HRNet) for feature extraction embedding with the UNet for segmentation purposes. To evaluate the proposed dataset, several baseline experiments have been performed employing numerous deep learning architectures. With extensive experiment, we got 99.26% accuracy, 98.53% sensitivity, and 98.82% specificity with HRNet which surpasses the performances of the existing models. Our proposed methodology ensures unbiased high accuracy, which increases the probability of incorporating X-Ray images into the diagnosis of the disease.