Sait, U and KV, GL and Shivakumar, S and Kumar, T and Bhaumik, R and Prajapati, S and Bhalla, K and Chakrapani, A (2021) A deep-learning based multimodal system for Covid-19 diagnosis using breathing sounds and chest X-ray images. In: Applied Soft Computing, 109 .
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Abstract
Covid-19 has become a deadly pandemic claiming more than three million lives worldwide. SARS-CoV-2 causes distinct pathomorphological alterations in the respiratory system, thereby acting as a biomarker to aid its diagnosis. A multimodal framework (Ai-CovScan) for Covid-19 detection using breathing sounds, chest X-ray (CXR) images, and rapid antigen test (RAnT) is proposed. Transfer Learning approach using existing deep-learning Convolutional Neural Network (CNN) based on Inception-v3 is combined with Multi-Layered Perceptron (MLP) to develop the CovScanNet model for reducing false-negatives. This model reports a preliminary accuracy of 80 for the breathing sound analysis, and 99.66 Covid-19 detection accuracy for the curated CXR image dataset. Based on Ai-CovScan, a smartphone app is conceptualised as a mass-deployable screening tool, which could alter the course of this pandemic. This app's deployment could minimise the number of people accessing the limited and expensive confirmatory tests, thereby reducing the burden on the severely stressed healthcare infrastructure. © 2021 Elsevier B.V.
| Item Type: | Journal Article |
|---|---|
| Publication: | Applied Soft Computing |
| Publisher: | Elsevier Ltd |
| Additional Information: | The copyright for this article belongs to Authors |
| Keywords: | Convolutional neural networks; Deep neural networks; E-learning; Learning systems; mHealth; Multilayer neural networks; Respiratory system; Transfer learning, Chest X-ray image; Confirmatory test; Detection accuracy; Healthcare infrastructure; Multi-layered Perceptron; Multimodal frameworks; Multimodal system; Number of peoples, Deep learning |
| Department/Centre: | Division of Mechanical Sciences > Centre for Product Design & Manufacturing |
| Date Deposited: | 24 Aug 2021 06:50 |
| Last Modified: | 24 Aug 2021 06:50 |
| URI: | http://eprints.iisc.ac.in/id/eprint/69231 |
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