{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T02:29:36Z","timestamp":1760236176366,"version":"build-2065373602"},"reference-count":17,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2021,10,26]],"date-time":"2021-10-26T00:00:00Z","timestamp":1635206400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>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\u2019s 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.<\/jats:p>","DOI":"10.3390\/fi13110269","type":"journal-article","created":{"date-parts":[[2021,10,26]],"date-time":"2021-10-26T23:49:52Z","timestamp":1635292192000},"page":"269","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["COVIDNet: Implementing Parallel Architecture on Sound and Image for High Efficacy"],"prefix":"10.3390","volume":"13","author":[{"given":"Manickam","family":"Murugappan","sequence":"first","affiliation":[{"name":"Department of Computing, UOW Malaysia, KDU Penang University College, Penang 10400, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9992-6094","authenticated-orcid":false,"given":"John Victor Joshua","family":"Thomas","sequence":"additional","affiliation":[{"name":"Department of Computing, UOW Malaysia, KDU Penang University College, Penang 10400, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0509-5662","authenticated-orcid":false,"given":"Ugo","family":"Fiore","sequence":"additional","affiliation":[{"name":"Department of Management and Quantitative Studies, Universit\u00e0 degli Studi di Napoli Parthenope, Via Gen. Parisi, 13, 38, 80133 Napoli, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yesudas Bevish","family":"Jinila","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Sathyabama Institute of Science and Technology, Jeppiaar Nagar, Rajiv Gandhi Salai, Chennai 600119, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Subhashini","family":"Radhakrishnan","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Sathyabama Institute of Science and Technology, Jeppiaar Nagar, Rajiv Gandhi Salai, Chennai 600119, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"103792","DOI":"10.1016\/j.compbiomed.2020.103792","article-title":"Automated detection of COVID-19 cases using deep neural networks with X-ray images","volume":"121","author":"Ozturk","year":"2020","journal-title":"Comput. Biol. Med."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1224","DOI":"10.1038\/s41591-020-0931-3","article-title":"Artificial intelligence\u2013enabled rapid diagnosis of patients with COVID-19","volume":"26","author":"Mei","year":"2020","journal-title":"Nat. Med."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"100378","DOI":"10.1016\/j.imu.2020.100378","article-title":"AI4COVID-19: AI enabled preliminary diagnosis for COVID-19 from cough samples via an app","volume":"20","author":"Imran","year":"2020","journal-title":"Inform. Med. Unlocked"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1122","DOI":"10.1016\/j.cell.2018.02.010","article-title":"Identifying medical diagnoses and treatable diseases by image-based deep learning","volume":"172","author":"Kermany","year":"2018","journal-title":"Cell"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Madani, A., Moradi, M., Karargyris, A., and Syeda-Mahmood, T. (2018, January 10\u201315). Chest X-ray generation and data augmentation for cardiovascular abnormality classification. Proceedings of the Medical Imaging 2018, Image Processing, Houston, TX, USA.","DOI":"10.1117\/12.2293971"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"3956","DOI":"10.3390\/app10113956","article-title":"Classification of Heart Sounds Using Convolutional Neural Network","volume":"10","author":"Tang","year":"2020","journal-title":"Appl. Sci."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Thomas, J.J., Karagoz, P., Ahamed, B.B., and Vasant, P. (2020). Deep Learning Techniques and Optimization Strategies in Big Data Analytics, IGI Glob.","DOI":"10.4018\/978-1-7998-1192-3"},{"key":"ref_8","unstructured":"Chen, G., Chen, P., Shi, Y., Hsieh, C.Y., Liao, B., and Zhang, S. (2019). Rethinking the usage of batch normalization and dropout in the training of deep neural networks. arXiv."},{"key":"ref_9","unstructured":"(2021, October 02). 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Available online: https:\/\/github.com\/JordanMicahBennett\/SMART-CT-SCAN_BASED-COVID19_VIRUS_DETECTOR."}],"container-title":["Future Internet"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-5903\/13\/11\/269\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:23:38Z","timestamp":1760167418000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-5903\/13\/11\/269"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,26]]},"references-count":17,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2021,11]]}},"alternative-id":["fi13110269"],"URL":"https:\/\/doi.org\/10.3390\/fi13110269","relation":{},"ISSN":["1999-5903"],"issn-type":[{"type":"electronic","value":"1999-5903"}],"subject":[],"published":{"date-parts":[[2021,10,26]]}}}