Breast cancer nuclei segmentation and classification based on a deep learning approach

DOI10.34768/amcs-2021-0007zbMath1470.92172OpenAlexW3188337644MaRDI QIDQ2042989

Józef Korbicz, Marcin Skobel, Artur Gramacki, Marek Kowal

Publication date: 22 July 2021

Published in: International Journal of Applied Mathematics and Computer Science (Search for Journal in Brave)

Full work available at URL: https://sciendo.com/pdf/10.34768/amcs-2021-0007

zbMATH Keywords

classificationimage processingbreast cancernuclei segmentation

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to actuarial sciences and financial mathematics (62P05) Biomedical imaging and signal processing (92C55)

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Uses Software

• R
• Fiji
• ElemStatLearn
• ISLR
• U-Net

Cites Work

• Unnamed Item
• Bounded-abstaining classification for breast tumors in imbalanced ultrasound images
• Efficient decision trees for multi-class support vector machines using entropy and generalization error estimation
• Nuclei segmentation for computer-aided diagnosis of breast cancer
• An Introduction to Statistical Learning

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