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http://conacyt.repositorioinstitucional.mx/jspui/handle/1000/2313| COVID-CAPS: A Capsule Network-based Framework for Identification of COVID-19 cases from X-ray Images | |
| Parnian Afshar. Shahin Heidarian. Farnoosh Naderkhani. Anastasia Oikonomou. Konstantinos N. Plataniotis. Arash Mohammadi. | |
| Acceso Abierto | |
| Atribución-NoComercial-SinDerivadas | |
| https://arxiv.org/pdf/2004.02696v1.pdf | |
| 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 and high false-negative rate. 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. This 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 initial 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%, Sensitivity of 90%, Specificity of 95.8%, and Area Under the Curve (AUC) of 0.97, while having far less number of trainable parameters in comparison to its counterparts. | |
| arxiv.org | |
| 2020 | |
| Artículo | |
| https://arxiv.org/pdf/2004.02696v1.pdf | |
| Inglés | |
| VIRUS RESPIRATORIOS | |
| Aparece en las colecciones: | Artículos científicos |
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