aspg-28-1906-1792155112 20261016125152 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Journal of Artificial Intelligence and Metaheuristics JAIM 2833-5597 2023 3 2 S. K. Towfek Computer Science and Intelligent Systems Research Center, Blacksburg 24060, Virginia, USA Ehsaneh khodadadi Department of Chemistry and Biochemistry, University of Arkansas, Fayetteville, AR 72701, USA Fatma M. Talaat Faculty of Artificial Intelligence, Kafrelsheikh University, Kafrelsheikh, Egypt The accurate and timely segmentation of COVID-19 infection areas from CT scans is crucial for effective diagnosis and treatment planning. In this paper, we propose an automated approach utilizing machine learning techniques for COVID-19 infection segmentation. The proposed framework utilizes a convolutional neural network (CNN) architecture to extract informative features from CT scan images. These features are then fed into a segmentation model, which employs a combination of U-Net and attention mechanisms for accurate delineation of infection regions. To enhance the model's performance, we employ a transfer learning strategy by pretraining the CNN on a large dataset of general medical images. To evaluate the effectiveness of our approach, we conducted experiments on a diverse dataset consisting of CT scans from COVID-19 patients. The results demonstrate the superiority of our method in accurately segmenting infection areas, achieving an average Dice coefficient of 0.92 and a Jaccard index of 0.88. The proposed automated segmentation method offers significant potential for aiding radiologists and clinicians in identifying COVID-19 infection regions from CT scans rapidly and accurately. It can contribute to improved diagnosis, patient management, and treatment planning in the fight against the ongoing pandemic. 2023 28 37 1906 https://creativecommons.org/licenses/by/4.0/ 10.54216/JAIM.030203 https://www.americaspg.com/journal/28/article/1906 https://www.americaspg.com/storage/71687650981.pdf