# COVID-19 Detection in Chest X-Ray Images using a New Channel Boosted CNN

arXiv: https://arxiv.org/abs/2012.05073 · HTML: https://mlsanity.com/paper/2012.05073/

## Key facts

- Cited by 15 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 8 Dec 2020.

## Built on by (most cited first, 15 of 15)

- [Segmentation of Shoulder Muscle MRI Using a New Region and Edge based Deep Auto-Encoder](https://mlsanity.com/paper/2108.11720/) (2021)
- [A Survey of Deep Learning Techniques for the Analysis of COVID-19 and their usability for Detecting Omicron](https://mlsanity.com/paper/2202.06372/) (2022)
- [COVID-19 Detection and Analysis From Lung CT Images using Novel Channel Boosted CNNs](https://mlsanity.com/paper/2209.10963/) (2022)
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- [COVID-19 Infection Analysis Framework using Novel Boosted CNNs and Radiological Images](https://mlsanity.com/paper/2302.02619/) (2023)
- [Deep Radiomic Analysis for Predicting Coronavirus Disease 2019 in Computerized Tomography and X-ray Images](https://mlsanity.com/paper/2206.01903/) (2022)
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- [A Novel Deep Hybrid Framework with Ensemble-Based Feature Optimization for Robust Real-Time Human Activity Recognition](https://mlsanity.com/paper/2508.18695/) (2025)
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## Abstract (arXiv)

COVID-19 is a highly contagious respiratory infection that has affected a large population across the world and continues with its devastating consequences. It is imperative to detect COVID-19 at the earliest to limit the span of infection. In this work, a new classification technique CB-STM-RENet based on deep Convolutional Neural Network (CNN) and Channel Boosting is proposed for the screening of COVID-19 in chest X-Rays. In this connection, to learn the COVID-19 specific radiographic patterns, a new convolution block based on split-transform-merge (STM) is developed. This new block systematically incorporates region and edge-based operations at each branch to capture the diverse set of features at various levels, especially those related to region homogeneity, textural variations, and boundaries of the infected region. The learning and discrimination capability of the proposed CNN architecture is enhanced by exploiting the Channel Boosting idea that concatenates the auxiliary channels along with the original channels. The auxiliary channels are generated from the pre-trained CNNs using Transfer Learning. The effectiveness of the proposed technique CB-STM-RENet is evaluated on three different datasets of chest X-Rays namely CoV-Healthy-6k, CoV-NonCoV-10k, and CoV-NonCoV-15k. The performance comparison of the proposed CB-STM-RENet with the existing techniques exhibits high performance both in discriminating COVID-19 chest infections from Healthy, as well as, other types of chest infections. CB-STM-RENet provides the highest performance on all these three datasets; especially on the stringent CoV-NonCoV-15k dataset. The good detection rate (97%), and high precision (93%) of the proposed technique suggest that it can be adapted for the diagnosis of COVID-19 infected patients. The test code is available at https://github.com/PRLAB21/COVID-19-Detection-System-using-Chest-X-Ray-Images.