Detail-Oriented Capsule Network for classification of CT scan images performing the detection of COVID-19.

COVID-19 is one of the biggest pandemics that the world is facing today, and every day, we are coming up with new challenges in this area. Still, much research is already going on to overcome this pandemic, and we also get succeeded to some extent. Diverse sources such as MRI, CT scanning, blood samples, X-ray image, and many more are available to detect COVID-19. Thus, it can be easily said that through image processing, the classification of COVID-19 can be done. In this study, the COVID-19 detection is done by classifying with the use of a type of convolutional neural network termed a detail-oriented capsule network. Chest CT scan imaging for the prediction of COVID-19 and non-COVID-19 are classified in the present paper using a Detailed Oriented capsule network (DOCN). Accuracy, specificity, and sensitivity are parameters used for model evaluation. The proposed model has achieved 98% accuracy, 81% sensitivity, and 98.4% specificity.

Feature extraction with capsule network for the COVID-19 disease prediction though X-ray images.

Past couple of years, the world is going through one of the biggest pandemic named COVID-19. In the mid of year 2019, it is a very difficult process to predict the COVID-19 just by viewing the images. Later on AI based technology has done a significant role in the prediction of COVID-19 through biomedical images such as CT scan, X ray etc. This study also implemented the deep learning model for the prediction of COVID-19 through X-ray images. The implemented model is termed as XR-CAPS which consist of two models such as U-Net model and the capsule network. The U Net model is used for performing the segmentation of the images and the capsule networks are applied for performing the feature extraction. The XR-CAPS model is applied on the X-ray images for the prediction of COVID-19 and the evaluation of the model is done by three parameters that are accuracy, sensitivity and specificity. The model is compared with other existing models like ResNet50, DenseNet121 and DenseCapsNet, this has achieved an accuracy of 93.2%, sensitivity of 94% and specificity of 97.1% which is better than other states of the art algorithms.