Automated segmentation of ultrasound medical images using the Attention U-Net model

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dc.contributor.authorMomot, A.
dc.contributor.authorZaboluieva, M.
dc.contributor.authorGalagan, R.
dc.date.accessioned2024-03-28T18:45:25Z
dc.date.available2024-03-28T18:45:25Z
dc.date.issued2024
dc.description.abstractThe article deals with the method of automated semantic segmentation of ultrasound medical images using the Attention U-Net deep learning model. The advantages of using Attention blocks in neural network architectures for segmentation tasks are analyzed. To test the described algorithms, the Breast Ultrasound Images training dataset was chosen. The method described in the article allows for automating the process of detecting and preliminary classification of breast tumors based on the analysis of ultrasound images. As a result of training the Attention U-Net model, the Mean IOU value of 49.2% was obtained on the test set. The network can automatically classify the detected neoplasm as benign or malignant with an F1 Score of 0.87. The results indicate the prospects of using the Attention U-Net model in the tasks of analyzing ultrasound medical images. Ways to further improve the considered method are proposed
dc.format.pagerangeP. 56-60
dc.identifier.citationMomot, A. Automated segmentation of ultrasound medical images using the attention u-net model / Momot A., Zaboluieva M., Galagan R. // Norwegian Journal of Development of the International Science. - 2024. - № 128. - P. 56-60.
dc.identifier.doihttps://doi.org/10.5281/zenodo.10817342
dc.identifier.urihttps://ela.kpi.ua/handle/123456789/65838
dc.language.isoen
dc.relation.ispartofNorwegian Journal of development of the International Science
dc.titleAutomated segmentation of ultrasound medical images using the Attention U-Net model
dc.typeArticle

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