medical-6

COVID-19 Diagnosis

3260 papers • 126 benchmarks • 313 datasets

Covid-19 Diagnosis is the task of diagnosing the presence of COVID-19 in an individual with machine learning.

(Image credit: Papersgraph)

Benchmarks

These leaderboards are used to track progress in covid-19-diagnosis-6

Trend

Dataset

Best Model

Actions

COVIDx CXR-3

COVIDx

Novel COVID-19 Chestxray Repository

Libraries

i

Use these libraries to find covid-19-diagnosis-6 models and implementations

AlexTS1980/COVID-CT-Mask-Net

3 papers 28

Datasets

COVIDx

COVID-19 Image Data Collection

Subtasks

No subtasks available.

Most implemented papers

COVID-CT-Dataset: A CT Scan Dataset about COVID-19

Xuehai He, Pengtao Xie, Xingyi Yang, Jinyu Zhao, Yichen Zhang•Sun Mar 29 2020

An open-sourced dataset, which contains 349 COVID-19 CT images from 216 patients and 463 non-COVID- 19 CTs, is built, which is used to develop diagnosis methods based on multi-task learning and self-supervised learning that achieve an F1 of 0.90, an AUC of0.98, and an accuracy of 1.89.

898

Content

COVID-19 CXR Dataset

Covid-19 Cough Cambridge

Large COVID-19 CT scan slice dataset

black0017/MedicalZooPytorch

2 papers 1,591

COVID-19-CT-CXR

Large COVID-19 CT scan slice dataset

Synthetic COVID-19 CXR Dataset

Novel COVID-19 Chestxray Repository

0

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

Linda Wang, Z. Q. Lin, A. Wong•Sat Mar 21 2020

COVID-Net is introduced, a deep convolutional neural network design tailored for the detection of COVID-19 cases from chest X-ray (CXR) images that is open source and available to the general public, and COVIDx, an open access benchmark dataset comprising of 13,975 CXR images across 13,870 patient patient cases.

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COVID-19 Image Data Collection

Joseph Paul Cohen, Paul Morrison, Lan Dao•Tue Mar 24 2020

The initial COVID-19 open image data collection was created by assembling medical images from websites and publications and currently contains 123 frontal view X-rays.

1052 0

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A critic evaluation of methods for COVID-19 automatic detection from X-ray images

L. Nanni, Gianluca Maguolo•Sun Apr 26 2020

It is deduced that several testing protocols for the recognition are not fair and that the neural networks are learning patterns in the dataset that are not correlated to the presence of COVID-19.

207 0

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Exploring Automatic Diagnosis of COVID-19 from Crowdsourced Respiratory Sound Data

Tong Xia, Chloë Brown, Jagmohan Chauhan, Andreas Grammenos, Jing Han, Apinan Hasthanasombat, Dimitris Spathis, Pietro Cicuta, C. Mascolo•Tue Jun 09 2020

The results show that even a simple binary machine learning classifier is able to classify correctly healthy and COVID-19 sounds, and opens the door to further investigation of how automatically analysed respiratory patterns could be used as pre-screening signals to aid CO VID-19 diagnosis.

426 0

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Deep Learning COVID-19 Features on CXR Using Limited Training Data Sets

J. C. Ye, Y. Oh, Sangjoon Park•Sun Apr 12 2020

Experimental results show that the proposed patch-based convolutional neural network approach achieves state-of-the-art performance and provides clinically interpretable saliency maps, which are useful for COVID-19 diagnosis and patient triage.

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A deep convolutional neural network for COVID-19 detection using chest X-rays

P. Bassi, R. Attux•Wed Apr 29 2020

The state-of-the-art performances achieved show that, with the help of artificial intelligence, chest X-rays can become a cheap and accurate auxiliary method for COVID-19 diagnosis and indicate an analytical path for future research on diagnosis.

148 0

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Exploration of Interpretability Techniques for Deep COVID-19 Classification Using Chest X-ray Images

P. Radeva, A. Nürnberger, S. Chatterjee, Rupali Khatun, Fatima Saad, Chompunuch Sarasaen, Suhita Ghosh, G. Rose, Sebastian Stober, O. Speck•Tue Jun 02 2020

Five different deep learning models and their ensemble and their ensemble and their ensemble and their ensemble and their ensemble have been used to classify COVID-19, pneumoniæ and healthy subjects using chest X-ray images, showing that the ResNets were the most interpretable models.

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COVID-CAPS: A capsule network-based framework for identification of COVID-19 cases from X-ray images

K. Plataniotis, Parnian Afshar, Shahin Heidarian, Farnoosh Naderkhani, A. Oikonomou, Arash Mohammadi•Sun Apr 05 2020

Results based on a dataset of X-ray images show that COVID-CAPS has advantage over previous CNN-based models, being capable of handling small datasets, which is of significant importance due to sudden and rapid emergence of CO VID-19.

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A Fully Automated Deep Learning-based Network For Detecting COVID-19 from a New And Large Lung CT Scan Dataset

Mohammad Rahimzadeh, Abolfazl Attar, S. M. Sakhaei•Thu Dec 31 2020

A high-speed and accurate fully-automated method to detect COVID-19 from the patient's chest CT scan images is proposed and a novel architecture for improving the classification accuracy of convolutional networks on images containing small important objects is introduced.

287 0

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Adding a benchmark result helps the community track progress.