1
Computer Science and Intelligent Systems Research Center, Blacksburg 24060, Virginia, USA
(sktowfek@jcsis.org)
2
Department of Chemistry and Biochemistry, University of Arkansas, Fayetteville, AR 72701, USA
(ekhodada@uark.edu)
3
Faculty of Artificial Intelligence, Kafrelsheikh University, Kafrelsheikh, Egypt
(fatma.nada@ai.kfs.edu.eg)
Abstract :
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.
Keywords :
Machine Learning (ML); COVID-19; Lung Segmentation; Computed Tomography; Lesion Segmentation.
References :
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| Style | # |
|---|---|
| MLA | S. K. Towfek, Ehsaneh khodadadi, Fatma M. Talaat. "Automated Detection and Segmentation of COVID-19 Infection using Machine Learning." Journal of Artificial Intelligence and Metaheuristics, Vol. 3, No. 2, 2023 ,PP. 28-37 (Doi : https://doi.org/10.54216/JAIM.030203) |
| APA | S. K. Towfek, Ehsaneh khodadadi, Fatma M. Talaat. (2023). Automated Detection and Segmentation of COVID-19 Infection using Machine Learning. Journal of Journal of Artificial Intelligence and Metaheuristics, 3 ( 2 ), 28-37 (Doi : https://doi.org/10.54216/JAIM.030203) |
| Chicago | S. K. Towfek, Ehsaneh khodadadi, Fatma M. Talaat. "Automated Detection and Segmentation of COVID-19 Infection using Machine Learning." Journal of Journal of Artificial Intelligence and Metaheuristics, 3 no. 2 (2023): 28-37 (Doi : https://doi.org/10.54216/JAIM.030203) |
| Harvard | S. K. Towfek, Ehsaneh khodadadi, Fatma M. Talaat. (2023). Automated Detection and Segmentation of COVID-19 Infection using Machine Learning. Journal of Journal of Artificial Intelligence and Metaheuristics, 3 ( 2 ), 28-37 (Doi : https://doi.org/10.54216/JAIM.030203) |
| Vancouver | S. K. Towfek, Ehsaneh khodadadi, Fatma M. Talaat. Automated Detection and Segmentation of COVID-19 Infection using Machine Learning. Journal of Journal of Artificial Intelligence and Metaheuristics, (2023); 3 ( 2 ): 28-37 (Doi : https://doi.org/10.54216/JAIM.030203) |
| IEEE | S. K. Towfek, Ehsaneh khodadadi, Fatma M. Talaat, Automated Detection and Segmentation of COVID-19 Infection using Machine Learning, Journal of Journal of Artificial Intelligence and Metaheuristics, Vol. 3 , No. 2 , (2023) : 28-37 (Doi : https://doi.org/10.54216/JAIM.030203) |