Revolutionizing COVID-19 Diagnosis: Advancements in Chest X-ray Analysis through Customized Convolutional Neural Networks and Image Fusion Data Augmentation

¹ ATISP Research Lab, National School of Electronics and Telecommunications, University of Sfax, Sfax 3029, Tunisia
² Department of Information Technology and Management Systems, Faculty of Business Administration, Al Maaref University, 1001 Beirut, Lebanon
³ Education Directorate of Thi-Qar, Ministry of Education, Iraq

^* Corresponding Author: Zainab_marid@sadiq.edu.iq

Abstract

COVID-19 is produced by a new coronavirus called SARS-CoV-2, has wrought extensive damage. Globally, Patients present a wide range of challenges, which has forced medical professionals to actively seek out cutting-edge therapeutic approaches and technology advancements. Machine learning technologies have significantly enhanced the comprehension and control of the COVID-19 issue. Machine learning enables computers to emulate human-like behavior by efficiently recognizing patterns and extracting valuable insights. Cognitive capacity and aptitude for handling substantial quantities of data. Amidst the battle against COVID-19, firms have promptly employed machine-learning expertise in several ways, such as improving consumer communication, enhance comprehension of the COVID-19 transmission mechanism and expedite research and treatment. This work is centered around the utilization of deep learning techniques for predictive modeling. in individuals impacted with COVID-19. A data augmentation phase is included, utilizing multiexposure picture fusion techniques. Chest X-ray images of healthy individuals and COVID-19 patients make up our dataset.