COVIDX: Computer-aided diagnosis of Covid-19 and its severity prediction with raw digital chest X-ray images (2020-12-25T00:00:00.000000Z)

TL;DR

An automatic CO VID-19 diagnostic and severity prediction (COVIDX) system that uses deep feature maps from CXR images to diagnose COVID-19 and its severity prediction and outperforms all the existing state-of-the-art methods designed for this purpose.

Abstract

Coronavirus disease (COVID-19) is a contagious infection caused by severe acute respiratory syndrome coronavirus-2 (SARS-COV-2) and it has infected and killed millions of people across the globe. In the absence of specific drugs or vaccines for the treatment of COVID-19 and the limitation of prevailing diagnostic techniques, there is a requirement for some alternate automatic screening systems that can be used by the physicians to quickly identify and isolate the infected patients. A chest X-ray (CXR) image can be used as an alternative modality to detect and diagnose the COVID-19. In this study, we present an automatic COVID-19 diagnostic and severity prediction (COVIDX) system that uses deep feature maps from CXR images to diagnose COVID-19 and its severity prediction. The proposed system uses a three-phase classification approach (healthy vs unhealthy, COVID-19 vs Pneumonia, and COVID-19 severity) using different shallow supervised classification algorithms. We evaluated COVIDX not only through 10-fold cross2 validation and by using an external validation dataset but also in real settings by involving an experienced radiologist. In all the evaluation settings, COVIDX outperforms all the existing stateof-the-art methods designed for this purpose. We made COVIDX easily accessible through a cloud-based webserver and python code available at https://sites.google.com/view/wajidarshad/software and https://github.com/wajidarshad/covidx, respectively.

Authors

Wajid Arshad Abbasi

1 papers

S. Abbas

1 papers

S. Andleeb

1 papers

TL;DR

Abstract

Authors

References43 items

Deep learning based detection and analysis of COVID-19 on chest X-ray images

Coronavirus disease (COVID-19) detection in Chest X-Ray images using majority voting based classifier ensemble

Classification assessment methods

Diagnostic techniques for COVID-19 and new developments

COVID-19 Image Data Collection: Prospective Predictions Are the Future

Application of deep learning for fast detection of COVID-19 in X-Rays using nCOVnet

COVID-19 detection using deep learning models to exploit Social Mimic Optimization and structured chest X-ray images using fuzzy color and stacking approaches

Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural networks

Automated detection of COVID-19 cases using deep neural networks with X-ray images

Deep-COVID: Predicting COVID-19 from chest X-ray images using deep transfer learning

Automated Methods for Detection and Classification Pneumonia based on X-Ray Images Using Deep Learning

Can AI Help in Screening Viral and COVID-19 Pneumonia?

Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network

COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images

Analysis of quantum noise-reducing filters on chest X-ray images: A review

First case of Coronavirus Disease 2019 (COVID-19) pneumonia in Taiwan

Coronavirus (COVID-19) Outbreak: What the Department of Radiology Should Know

Recent advances in the detection of respiratory virus infection in humans

Identifying pneumonia in chest X-rays: A deep learning approach

Localizing tuberculosis in chest radiographs with deep learning

Learning to detect chest radiographs containing pulmonary lesions using visual attention networks

Learning protein binding affinity using privileged information

Learning Transferable Architectures for Scalable Image Recognition

Xception: Deep Learning with Depthwise Separable Convolutions

Densely Connected Convolutional Networks

Issues in performance evaluation for host-pathogen protein interaction prediction

XGBoost: A Scalable Tree Boosting System

Deep Residual Learning for Image Recognition

Rethinking the Inception Architecture for Computer Vision

Very Deep Convolutional Networks for Large-Scale Image Recognition

Substituting random forest for multiple linear regression improves binding affinity prediction of scoring functions: Cyscore as a case study

Scikit-learn: Machine Learning in Python

A machine learning approach to predicting protein-ligand binding affinity with applications to molecular docking

The relationship between Precision-Recall and ROC curves

Greedy function approximation: A gradient boosting machine.

Support-Vector Networks

Pneumonia Detection on Chest X-Ray Using Machine Learning Paradigm

Protein-protein binding affinity prediction on a diverse set of structures. Bioinformatics btr513

Random Forests

Object Recognition with Gradient-Based Learning

Clinical features of patients infected with 2019 novel coronavirus in Wuhan

Coronavirus Disease 2019 Testing Basics. FDA. Coronavirus disease (COVID-19) -World Health Organization

Johns Hopkins Coronavirus Resource Center

Field of Study

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