###### Abstract  
The novel coronavirus (COVID-19) is a contagious viral disease that has rapidly spread worldwide since December 2019, causing the disruption of life and heavy economic losses. Since the beginning of the virus outbreak, a polymerase chain reaction has been used to detect the virus. However, since it is an expensive and slow method, artificial intelligence researchers have attempted to develop quick, inexpensive alternative methods of diagnosis to help doctors identify positive cases. Therefore, researchers are starting to incorporate chest X-ray scans (CCRs), an easy and inexpensive examination method. This study used an approach that uses image cropping methods and a deep learning technique (updated VGG16 model) to classify three public datasets. This study had four main steps. First, the data were split into training and testing sets (70% and 30%, respectively). Second, in the image processing step, each image was cropped to show only the chest area. The images were then resized to 150 \(\times\) 150. The third step was to build an updated VGG16 convolutional neural network (VGG16-CNN) model using multiple classifications (three classes: COVID-19, normal, and pneumonia) and binary classification (COVID-19 and normal). The fourth step was to evaluate the model's performance using accuracy, sensitivity, and specificity. This study obtained 97.50% accuracy for multiple classifications and 99.76% for binary classification. The study also got the best COVID-19 classification accuracy (99%) for both models. It can be considered that the scientific contribution of this research is summarized as: the VGG16 model was reduced from approximately 138 million parameters to around 40 million parameters. Further, it was tested on three different datasets and proved highly efficient in performance. coronavirus; COVID-19; image processing; deep learning; CNN; VGG16 20222129320222129322129322129322129322129321221293221293221293221293212212932212932122129322129321221293212212932212932122129322129322129  
X-ray images, among other techniques [7]. As COVID-19 spread, online X-ray images of COVID and non-COVID patients became publicly available for analysis in the form of databases on various repositories, such as GitHub and Kaggle [1; 3]. According to the latest statistics from the WHO, at 4:14 PM CET on 29 December 21, the number of people infected with COVID-19 officially reported worldwide had reached 281,808,270, and the number of deaths had reached 5,411,759 (https://worldhealthorg. shinyapps.io/covid/, accessed on 13 September 2022). Detecting COVID-19 cases based on X-ray imaging outperforms other virus detection systems and testing groups because it gives faster results, can be used with many patients at the same time and has a low average cost that enables emerging countries to speed up the positive case detection process [4]. The deep learning and machine learning methods used to detect positive COVID-19 cases using computerized tomography (CT), X-ray, and CX images have helped doctors make appropriate decisions. Clinical X-rays are one of the most important and common methods of detecting pneumonia [8]. Deep learning methods, especially convolutional neural networks (CNNs), have performed well in analyzing and classifying X-ray images [9]. This study presents an updated deep-learning model to detect positive cases derived from the VGG16 model. The main objective adopted to build the proposed model is to reduce the training time by minimizing the parameters. The number of parameters in the VGG16 model is approximately 138 million, while the number of parameters in the proposed model is about 40 million. Therefore, reducing the number of parameters to a third is a scientific contribution to this study. In this study, three datasets were classified, each consisting of X-ray images of patients (coronavirus, pneumonia, and normal) using an updated VGG16 model. Two classification methods were used: multi-classification (three classes) and binary classification (cases infected with coronavirus vs. normal cases).