future internet Article COVIDNet: Implementing Parallel Architecture on Sound and Image for High Efficacy Manickam Murugappan 1 , John Victor Joshua Thomas 1 , Ugo Fiore 2, * , Yesudas Bevish Jinila 3 and Subhashini Radhakrishnan 3   Citation: Murugappan, M.; Thomas, J.V.J.; Fiore, U.; Jinila, Y.B.; Radhakrishnan, S. COVIDNet: Implementing Parallel Architecture on Sound and Image for High Efficacy. Future Internet 2021, 13, 269. https://doi.org/10.3390/fi13110269 Academic Editors: Remus Brad and Arpad Gellert Received: 14 September 2021 Accepted: 22 October 2021 Published: 26 October 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). 1 Department of Computing, UOW Malaysia, KDU Penang University College, Penang 10400, Malaysia; manickam1999@gmail.com (M.M.); jjoshua@kdupg.edu.my (J.V.J.T.) 2 Department of Management and Quantitative Studies, Università degli Studi di Napoli Parthenope, Via Gen. Parisi, 13, 38, 80133 Napoli, Italy 3 Departmentof Information Technology, Sathyabama Instituteof Science and Technology, Jeppiaar Nagar, Rajiv Gandhi Salai, Chennai 600119, India; bevishjinila.it@sathyabamauniversity.ac.in (Y.B.J.); ithod@sathyabama.ac.in (S.R.) * Correspondence: ugo.fiore@uniparthenope.it 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 1. Introduction Over the past several decades, research has laid the foundation for a broadly defined new direction of artificial intelligence technology. These parallel architectures on deep learning algorithms have been actively studied because of its unique and fascinating prop- erties as well as complementary applications in computer vision-based medical imageries. Medical imaging is critical for seeing internal organs without causing injury and detecting anomalies in their structure or function throughout the body. MRI, PET, and other medical imaging technologies can be used to obtain medical images. Scanners for X-rays, CT scans, and ultrasounds. This work involved four phases to complete. The first step is to acquire dataset for CXR images with three different classifications (COVID-19 pneumonia, Future Internet 2021, 13, 269. https://doi.org/10.3390/fi13110269 https://www.mdpi.com/journal/futureinternet