COVIDNet: Implementing Parallel Architecture on Sound and Image for High Efficacy

Manickam Murugappan, John Victor Joshua Thomas, Ugo Fiore, Yesudas Bevish Jinila and Subhashini Radhakrishnan
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Manickam Murugappan: Department of Computing, UOW Malaysia, KDU Penang University College, Penang 10400, Malaysia
John Victor Joshua Thomas: Department of Computing, UOW Malaysia, KDU Penang University College, Penang 10400, Malaysia
Ugo Fiore: Department of Management and Quantitative Studies, Università degli Studi di Napoli Parthenope, Via Gen. Parisi, 13, 38, 80133 Napoli, Italy
Yesudas Bevish Jinila: Department of Information Technology, Sathyabama Institute of Science and Technology, Jeppiaar Nagar, Rajiv Gandhi Salai, Chennai 600119, India
Subhashini Radhakrishnan: Department of Information Technology, Sathyabama Institute of Science and Technology, Jeppiaar Nagar, Rajiv Gandhi Salai, Chennai 600119, India

Future Internet, 2021, vol. 13, issue 11, 1-14

Abstract: The present work relates to the implementation of core parallel architecture in a deep learning algorithm. At present, deep learning technology forms the main interdisciplinary basis of healthcare, hospital hygiene, biological and medicine. This work establishes a baseline range by training hyperparameter space, which could be support images, and sound with further develop a parallel architectural model using multiple inputs with and without the patient’s involvement. The chest X-ray images input could form the model architecture include variables for the number of nodes in each layer and dropout rate. Fourier transformation Mel-spectrogram images with the correct pixel range use to covert sound acceptance at the convolutional neural network in embarrassingly parallel sequences. COVIDNet the end user tool has to input a chest X-ray image and a cough audio file which could be a natural cough or a forced cough. Three binary classification models (COVID-19 CXR, non-COVID-19 CXR, COVID-19 cough) were trained. The COVID-19 CXR model classifies between healthy lungs and the COVID-19 model meanwhile the non-COVID-19 CXR model classifies between non-COVID-19 pneumonia and healthy lungs. The COVID-19 CXR model has an accuracy of 95% which was trained using 1681 COVID-19 positive images and 10,895 healthy lungs images, meanwhile, the non-COVID-19 CXR model has an accuracy of 91% which was trained using 7478 non-COVID-19 pneumonia positive images and 10,895 healthy lungs. The reason why all the models are binary classification is due to the lack of available data since medical image datasets are usually highly imbalanced and the cost of obtaining them are very pricey and time-consuming. Therefore, data augmentation was performed on the medical images datasets that were used. Effects of parallel architecture and optimization to improve on design were investigated.

Keywords: embarrassingly parallel; hyperparameters; optimization; convolutional networks (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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