Convolutional capsule network for covid 19 detection
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This document proposes a convolutional capsule network called VGG-CapsNet to diagnose COVID-19 using chest X-ray images. Convolutional neural networks currently used for COVID-19 detection have limitations related to view-invariance and information loss during downsampling. VGG-CapsNet aims to address these issues by using a capsule network architecture. In simulations, VGG-CapsNet achieved 97% accuracy for COVID-19 vs. non-COVID-19 classification and 92% accuracy for COVID-19 vs. normal vs. viral pneumonia classification, outperforming a CNN-CapsNet model. The proposed VGG-CapsNet system is available online and can help detect COVID-19 in the body through chest radiographic images
Convolutional capsule network for covid 19 detection
- 1. Convolutional capsule network for COVID-19 detection using radiography images Shamik Tiwari | Anurag Jain Department of Virtualization, School of Computer Science, University of Petroleum and Energy Studies, Dehradun, Uttarakhand, India
- 2. Abstract Novel corona virus COVID-19 has spread rapidly all over the world. Due to increasing COVID-19 cases, there is a dearth of testing kits. Therefore, there is a severe need for an automatic recognition system as a solution to reduce the spreading of the COVID-19 virus. This work offers a decision support system based on the X-ray image to diagnose the presence of the COVID-19 virus. A deep learning-based computer-aided decision support system will be capable to differentiate between COVID-19 and pneumonia. Recently, convolutional neural network (CNN) is designed for the diagnosis of COVID-19 patients through chest radiography (or chest X-ray, CXR) images. However, due to the usage of CNN, there are some limitations with these decision support systems. These systems suffer with the problem of view-invariance and loss of information due to down-sampling. In this paper, the capsule network (CapsNet)- based system named visual geometry group capsule network (VGG-CapsNet) for the diagnosis of COVID-19 is proposed. Due to the usage of capsule network (CapsNet), the authors have succeeded in removing he drawbacks found in the CNN-based decision support system for the detection of COVID-19. Through simulation results, it is found that VGG-CapsNet has performed better than the CNN-CapsNet model for the diagnosis of COVID-19. The proposed VGG- CapsNet-based system has shown 97% accuracy for COVID-19 versus non-COVID-19 classification, and 92% accuracy for COVID-19 versus normal versus viral pneumonia classification. Proposed VGG-CapsNet-based system available at https://github.com/shamiktiwari/COVID19_Xray can be used to detect the existence of COVID-19 virus in the human body throughchest radiographic images.
- 3. Tiwari, S., & Jain, A. (2021). Convolutional capsule network for COVID‐19 detection using radiography images. International Journal of Imaging Systems and Technology.