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In Journal of computational science

The limitations of proper detectors for COVID-19 for the proliferating number of patients provoked us to build an auto-diagnosis system to detect COVID-19 infection using only one parameter. Our designed model is based on Deep Convolution Neural Network and considers lung/respiratory sound as the deterministic input for our approach. 'D-Cov19Net' has been trained with 23,592 recordings, begetting an AUC of 0.972 and sensitivity of 0.983 after 100 epochs. The model can be of immense utility in biomedical technology due to its significant accuracy, simplicity, user convenience, feasibility, and faster detection while maintaining social distancing.

Chatterjee Sukanya, Roychowdhury Jishnu, Dey Anilesh


Auto-diagnosis system, COVID-19 Detection, Convolution Neural Network (CNN), Deep Learning, Lung/Respiratory sound