swMATH ID: 41861
Software Authors: Parnian Afshar, Shahin Heidarian, Farnoosh Naderkhani, Anastasia Oikonomou, Konstantinos N. Plataniotis, Arash Mohammadi
Description: COVID-CAPS: A Capsule Network-based Framework for Identification of COVID-19 cases from X-ray Images. Novel Coronavirus disease (COVID-19) has abruptly and undoubtedly changed the world as we know it at the end of the 2nd decade of the 21st century. COVID-19 is extremely contagious and quickly spreading globally making its early diagnosis of paramount importance. Early diagnosis of COVID-19 enables health care professionals and government authorities to break the chain of transition and flatten the epidemic curve. The common type of COVID-19 diagnosis test, however, requires specific equipment and has relatively low sensitivity. Computed tomography (CT) scans and X-ray images, on the other hand, reveal specific manifestations associated with this disease. Overlap with other lung infections makes human-centered diagnosis of COVID-19 challenging. Consequently, there has been an urgent surge of interest to develop Deep Neural Network (DNN)-based diagnosis solutions, mainly based on Convolutional Neural Networks (CNNs), to facilitate identification of positive COVID-19 cases. CNNs, however, are prone to lose spatial information between image instances and require large datasets. The paper presents an alternative modeling framework based on Capsule Networks, referred to as the COVID-CAPS, being capable of handling small datasets, which is of significant importance due to sudden and rapid emergence of COVID-19. Our results based on a dataset of X-ray images show that COVID-CAPS has advantage over previous CNN-based models. COVID-CAPS achieved an Accuracy of 95.7
Homepage: https://arxiv.org/abs/2004.02696
Source Code:  https://github.com/ShahinSHH/COVID-CAPS
Related Software: CovXNet; COVID-Net; GitHub; Xception; AlexNet; Grad-CAM; ImageNet; Simulink; Matlab; ReCoNet; CAiRE-COVID; COVID-CXNet; CovidGAN; CovidCTNet; Covidex; Apache Spark; V-Net; XGBoost
Cited in: 0 Documents