Manchester Metropolitan University's Research Repository

    Chest X ray and cough sample based deep learning framework for accurate diagnosis of COVID-19

    Kumar, Santosh, Nagar, Rishab, Bhatnagar, Saumya, Vaddi, Ramesh, Gupta, Sachin Kumar, Rashid, Mamoon, Bashir, Ali Kashif ORCID logoORCID: https://orcid.org/0000-0001-7595-2522 and Alkhalifah, Tamim (2022) Chest X ray and cough sample based deep learning framework for accurate diagnosis of COVID-19. Computers and Electrical Engineering, 103. p. 108391. ISSN 0045-7906

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    All witnessed the terrible effects of the COVID-19 pandemic on the health and work lives of the population across the world. It is hard to diagnose all infected people in real time since the conventional medical diagnosis of COVID-19 patients takes a couple of days for accurate diagnosis results. In this paper, a novel learning framework is proposed for the early diagnosis of COVID-19 patients using hybrid deep fusion learning models. The proposed framework performs early classification of patients based on collected samples of chest X-ray images and Coswara cough (sound) samples of possibly infected people. The captured cough samples are pre-processed using speech signal processing techniques and Mel frequency cepstral coefficient features are extracted using deep convolutional neural networks. Finally, the proposed system fuses extracted features to provide 98.70% and 82.7% based on Chest-X ray images and cough (audio) samples for early diagnosis using the weighted sum-rule fusion method.

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