Journal of Artificial Intelligence and Metaheuristics
Journal DOI
2833-5597ISSN (Online)
Volume 3 , Issue 2 , PP: 28-37, 2023 | Cite this article as | XML | Html | PDF | Full Length Article
S. K. Towfek 1 * , Ehsaneh khodadadi 2 , Fatma M. Talaat 3
Doi: https://doi.org/10.54216/JAIM.030203
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.
Machine Learning (ML) , COVID-19 , Lung Segmentation , Computed Tomography , , Lesion Segmentation.
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