This repo is the official Implementation of the paper - COVID-WideNet—A capsule network for COVID-19 detection
🏆 SOTA for COVID-19 Diagnosis on COVIDx (AUC metric) check out papers with code
In this paper, we propose a capsule network called COVID-WideNet for diagnosing COVID-19 cases using Chest X-ray (CXR) images. Experimental results have demonstrated that a discriminative trained, multi-layer capsule network achieves state-of-the-art performance on the COVIDx dataset. In particular, COVID-WideNet performs better than any other CNN based approaches for the diagnosis of COVID-19 infected patients. Further, the proposed COVID-WideNet has the number of trainable parameters that is 20 times less than that of other CNN based models. This results in a fast and efficient diagnosing COVID-19 symptoms, and with achieving the 0.95 of Area Under Curve (AUC), 91% of accuracy, sensitivity and specificity, respectively. This may also assist radiologists to detect COVID and its variant like delta.
An open access benchmark dataset comprising of 13,975 CXR images across 13,870 patient cases, with the largest number of publicly available COVID-19 positive cases to the best of the authors' knowledge.
The dataset can be downloaded from the following link: https://paperswithcode.com/dataset/covidx
@article{gupta2022covid,
title={COVID-WideNet—A capsule network for COVID-19 detection},
author={Gupta, PK and Siddiqui, Mohammad Khubeb and Huang, Xiaodi and Morales-Menendez, Ruben and Pawar, Harsh and Terashima-Marin, Hugo and Wajid, Mohammad Saif},
journal={Applied Soft Computing},
volume={122},
pages={108780},
year={2022},
publisher={Elsevier}
}
Base Code Inspiration:
capsulelayers.py inspired from https://github.com/XifengGuo/CapsNet-Keras/blob/master/capsulelayers.py
train.py inspired from changspencer/Tumor-CapsNet#3
preprocess.py inspired from https://github.com/ShahinSHH/COVID-CAPS