Research Article DCNN-FuzzyWOA: Artificial Intelligence Solution for Automatic Detection of COVID-19 Using X-Ray Images Abbas Saffari , 1 Mohammad Khishe , 1 Mokhtar Mohammadi , 2 Adil Hussein Mohammed , 3 and Shima Rashidi 4 1 Department of Electrical Engineering, Imam Khomeini Marine Science University, Nowshahr, Iran 2 Department of Information Technology, College of Engineering and Computer Science, Lebanese French University, Erbil, Kurdistan Region, Iraq 3 Department of Communication and Computer Engineering, Faculty of Engineering, Cihan University-Erbil, Erbil, Kurdistan Region, Iraq 4 Department of Computer Science, College of Science and Technology, University of Human Development, Sulaymaniyah, Kurdistan Region, Iraq Correspondence should be addressed to Abbas Saffari; abbas.saffari@birjand.ac.ir Received 9 February 2022; Revised 1 June 2022; Accepted 14 June 2022; Published 9 August 2022 Academic Editor: Ahmed A. Ewees Copyright © 2022 Abbas Saffari et al. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Artificial intelligence (AI) techniques have been considered effective technologies in diagnosing and breaking the transmission chain of COVID-19 disease. Recent research uses the deep convolution neural network (DCNN) as the discoverer or classifier of COVID-19 X-ray images. e most challenging part of neural networks is the subject of their training. Descent-based (GDB) algorithms have long been used to train fullymconnected layer (FCL) at DCNN. Despite the ability of GDBs to run and converge quickly in some applications, their disadvantage is the manual adjustment of many parameters. erefore, it is not easy to parallelize them with graphics processing units (GPUs). erefore, in this paper, the whale optimization algorithm (WOA) evolved by a fuzzy system called FuzzyWOA is proposed for DCNN training. With accurate and appropriate tuning of WOA’s control parameters, the fuzzy system defines the boundary between the exploration and extraction phases in the search space. It causes the development and upgrade of WOA. To evaluate the performance and capability of the proposed DCNN-FuzzyWOA model, a publicly available database called COVID-Xray-5k is used. DCNN-PSO, DCNN-GA, and LeNet-5 benchmark models are used for fair comparisons. Comparative parameters include accuracy, processing time, standard deviation (STD), curves of ROC and precision-recall, and F1-Score. e results showed that the FuzzyWOA training algorithm with 20 epochs was able to achieve 100% accuracy, at a processing time of 880.44 s with an F1-Score equal to 100%. Structurally, the i-6c-2s-12c-2s model achieved better results than the i-8c-2s-16c-2s model. However, the results of using FuzzyWOA for both models have been very encouraging compared to particle swarm optimization, genetic algorithm, and LeNet-5 methods. 1. Introduction COVID-19 was initially designated an epidemic disease by the World Health Organization (WHO) in March 2020 [1]. Due to the increasing number of deaths, the spread of the disease, the lack of access to vaccines and particular drugs, and rapid diagnosis of the disease to break, the transmission chain has become one of the most important research topics for researchers. Polymerase chain reaction (PCR) test [2] and X-ray images [3] are standard methods in detecting COVID-19. One of the problems of PCR tests is that there are not enough kits and also it takes a relatively long time to answer the test. In addition to being affordable, X-ray images are always and everywhere available. Reducing the time to diagnose and detect positive cases, even without fever and cough symptoms, are other benefits of using X-ray images [4]. AI tools can increase processing time and high accuracy in detecting patients with COVID-19 [5]. Much research has been done to identify positive cases of COVID-19 [3, 6]. However, until COVID-19 disease is completely eradicated, Hindawi Computational Intelligence and Neuroscience Volume 2022, Article ID 5677961, 11 pages https://doi.org/10.1155/2022/5677961