Semnan University International Journal of Nonlinear Analysis and Applications 2008-6822 14 1 2023 01 01 COVID-19IraqKirkukDataset: Development and evaluation of an Iraqi dataset for COVID-19 classification based on deep learning 2507 2518 7317 10.22075/ijnaa.2023.7317 EN Mohammed Saleh Ahmed Computer Science Department, College of Computer Science and Information Technology, Kirkuk University, Kirkuk, Iraq Ahmed M. Fakhrudeen Software Department, College of Computer Science and Information Technology, Kirkuk University, Kirkuk, Iraq Journal Article 2022 11 03 In the last two years, the coronavirus (COVID-19) pandemic put healthcare systems around the world under tremendous pressure. There have been intelligent systems (Machine Learning (ML) and Deep Learning (DL)) able to identify COVID-19 from similar normal diseases. The algorithms use Imaging techniques (like Chest X-Rays) in classifying COVID-19. Therefore, many global COVID-19 datasets have been released. However, so far, no public local Iraqi dataset has been developed. Therefore, our contribution is two folds. First, we investigate the techniques of deep learning techniques in COVID-19 classification. Second, we develop a new COVID-19 dataset, namely, “Covid-19IraqKirkukDataset” collected from hospitals in Kirkuk, Iraq. To the best of our knowledge, our dataset is the first COVID-19 dataset. Then, the evaluation of Covid19IraqKirkukDataset using Convolutional Neural Networks (CNNs) demonstrates promising classification outcomes. COVID-19 Deep learning Convolutional Neural Networks Dataset X-rays https://ijnaa.semnan.ac.ir/article_7317_d207b421b3ca09038034be5cd044fe72.pdf