An Effective Deep Learning Model for Health Monitoring and Detection of COVID-19 Infected Patients: An End-to-End Solution

Vidyadevi G Biradar; Mejdal A Alqahtani; H C Nagaraj; Emad A Ahmed; Vikas Tripathi; Miguel Botto-Tobar; Henry Kwame Atiglah

doi:10.1155/2022/7126259

An Effective Deep Learning Model for Health Monitoring and Detection of COVID-19 Infected Patients: An End-to-End Solution

Comput Intell Neurosci. 2022 Aug 11:2022:7126259. doi: 10.1155/2022/7126259. eCollection 2022.

Authors

Vidyadevi G Biradar¹, Mejdal A Alqahtani², H C Nagaraj³, Emad A Ahmed⁴, Vikas Tripathi⁵, Miguel Botto-Tobar^{6

7}, Henry Kwame Atiglah⁸

Affiliations

¹ Department of Information Science and Engineering, Nitte Meenakshi Institute of Technology, Bangalore, India.
² Department of Industrial Engineering, King Saud University, Riyadh, Saudi Arabia.
³ Nitte Meenakshi Institute of Technology, Bangalore, India.
⁴ Department of Computer Science, Faculty of Computers and Information, South Valley University, Qena, Egypt.
⁵ Department of Computer Science and Engineering, Graphic Era Deemed to Be University, Dehradun, Uttarakhand, India.
⁶ Eindhoven University of Technology, Eindhoven, Netherlands.
⁷ Research Group in Artificial Intelligence and Information Technology, University of Guayaquil, Guayaquil, Ecuador.
⁸ Department of Electrical and Electronics Engineering, Tamale Technical University, Ghana, Ghana.

Abstract

The COVID-19 infection is the greatest danger to humankind right now because of the devastation it causes to the lives of its victims. It is important that infected people be tested in a timely manner in order to halt the spread of the disease. Physical approaches are time-consuming, expensive, and tedious. As a result, there is a pressing need for a cost-effective and efficient automated tool. A convolutional neural network is presented in this paper for analysing X-ray pictures of patients' chests. For the analysis of COVID-19 infections, this study investigates the most suitable pretrained deep learning models, which can be integrated with mobile or online apps and support the mobility of diagnostic instruments in the form of a portable tool. Patients can use the smartphone app to find the nearest healthcare testing facility, book an appointment, and get instantaneous results, while healthcare professionals can keep track of the details thanks to the web and mobile applications built for this study. Medical practitioners can apply the COVID-19 detection model for chest frontal X-ray pictures with ease. A user-friendly interface is created to make our end-to-end solution paradigm work. Based on the data, it appears that the model could be useful in the real world.

MeSH terms

COVID-19* / diagnosis
Deep Learning*
Humans
Mobile Applications*
Neural Networks, Computer
Thorax