Papers › COVID-CXNet: Detecting COVID-19 in Frontal Chest X-ray Images using Deep Learning

COVID-CXNet: Detecting COVID-19 in Frontal Chest X-ray Images using Deep Learning

16 Jun 2020arXiv:2006.13807archive 2025-07-28

Arman Haghanifar, Mahdiyar Molahasani Majdabadi, Younhee Choi, S. Deivalakshmi, Seokbum Ko

One of the primary clinical observations for screening the infectious by the novel coronavirus is capturing a chest x-ray image. In most of the patients, a chest x-ray contains abnormalities, such as consolidation, which are the results of COVID-19 viral pneumonia. In this study, research is conducted on efficiently detecting imaging features of this type of pneumonia using deep convolutional neural networks in a large dataset. It is demonstrated that simple models, alongside the majority of pretrained networks in the literature, focus on irrelevant features for decision-making. In this paper, numerous chest x-ray images from various sources are collected, and the largest publicly accessible dataset is prepared. Finally, using the transfer learning paradigm, the well-known CheXNet model is utilized for developing COVID-CXNet. This powerful model is capable of detecting the novel coronavirus pneumonia based on relevant and meaningful features with precise localization. COVID-CXNet is a step towards a fully automated and robust COVID-19 detection system.

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Code

armiro/COVID-CXNet officialmentioned in papermentioned on GitHubtf report

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Tasks

Decision Making Image Classification Multi-class Classification Pneumonia Detection Transfer Learning

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Multi-class Classification	COVID-19 CXR Dataset	COVID-CXNet	Accuracy (%)	94.2	#1 of 1	Archive leaderboard	report
Pneumonia Detection	COVID-19 CXR Dataset	COVID-CXNet	F-Score	0.85	#1 of 1	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

1x1 Convolution Average Pooling Batch Normalization CheXNet Concatenated Skip Connection Convolution Dense Block Dense Connections Dropout Global Average Pooling Kaiming Initialization Label Smoothing Max Pooling ReLU Softmax U-Net

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