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COVID-19 is a highly contagious respiratory infection that has affected alarge population across the world and continues with its devastatingconsequences. It is imperative to detect COVID-19 at the earliest to limit thespan of infection. In this work, a new classification technique CB-STM-RENetbased on deep Convolutional Neural Network (CNN) and Channel Boosting isproposed for the screening of COVID-19 in chest X-Rays. In this connection, tolearn the COVID-19 specific radiographic patterns, a new convolution blockbased on split-transform-merge (STM) is developed. This new blocksystematically incorporates region and edge-based operations at each branch tocapture the diverse set of features at various levels, especially those relatedto region homogeneity, textural variations, and boundaries of the infectedregion. The learning and discrimination capability of the proposed CNNarchitecture is enhanced by exploiting the Channel Boosting idea thatconcatenates the auxiliary channels along with the original channels. Theauxiliary channels are generated from the pre-trained CNNs using TransferLearning. The effectiveness of the proposed technique CB-STM-RENet is evaluatedon three different datasets of chest X-Rays namely CoV-Healthy-6k,CoV-NonCoV-10k, and CoV-NonCoV-15k. The performance comparison of the proposedCB-STM-RENet with the existing techniques exhibits high performance both indiscriminating COVID-19 chest infections from Healthy, as well as, other typesof chest infections. CB-STM-RENet provides the highest performance on all thesethree datasets; especially on the stringent CoV-NonCoV-15k dataset. The gooddetection rate (97%), and high precision (93%) of the proposed techniquesuggest that it can be adapted for the diagnosis of COVID-19 infected patients.The test code is available athttps://github.com/PRLAB21/COVID-19-Detection-System-using-Chest-X-Ray-Images.

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