Advancing precision in medical image segmentation: A performance analysis of loss functions for COVID‐19 lung infection segmentation in computed tomography images

Advancing precision in medical image segmentation: A performance analysis of loss functions for COVID‐19 lung infection segmentation in computed tomography images

Abstract This study evaluates the effectiveness of three loss functions Asymmetric Unified Focal Loss (AUFL), Dice Similarity Coefficient Loss (DSCL), and Cross‐Entropy (CE) for segmenting COVID‐19 lung infections in computed tomography images. Detailed analyses using the intersection over union met...

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Bibliographic Details
Main Authors: Emilio Delgado, Roberto Rodriguez‐Echeverria, Antonio Jesús Fernández‐García, Juan D. Gutiérrez, Miguel Ángel Suero‐Rodrigo
Format: Article
Language:English
Published: Wiley 2024-11-01
Series:IET Image Processing
Subjects:
biomedical imaging
computerized tomography
computer vision
image segmentation
medical image processing
radiology
Online Access:https://doi.org/10.1049/ipr2.13232
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https://doi.org/10.1049/ipr2.13232

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