Digital Library of Zielona Góra contains 9 272 digital objects
Object
Title: Breast cancer nuclei segmentation and classification based on a deep learning approach
Creator:
Kowal, Marek ; Skobel, Marcin ; Gramacki, Jarosław ; Korbicz, Józef (1951- )
Date:
Resource Type:
Contributor:
Korbicz, Józef (1951- ) - red. ; Uciński, Dariusz - red.
Group publication title:
Abstract:
One of the most popular methods in the diagnosis of breast cancer is fine-needle biopsy without aspiration. Cell nuclei are the most important elements of cancer diagnostics based on cytological images. Therefore, the first step of successful classification of cytological images is effective automatic segmentation of cell nuclei. ; The aims of our study include (a) development of segmentation methods of cell nuclei based on deep learning techniques, (b) extraction of some morphometric, colorimetric and textural features of individual segmented nuclei, (c) based on the extracted features, construction of effective classifiers for detecting malignant or benign cases. The segmentation methods used in this paper are based on (a) fully convolutional neural networks and (b) the marker-controlled watershed algorithm. ; For the classification task, seven various classification methods are used. Cell nuclei segmentation achieves 90% accuracy for benign and 86% for malignant nuclei according to the F-score. The maximum accuracy of the classification reached 80.2% to 92.4%, depending on the type (malignant or benign) of cell nuclei. The classification of tumors based on cytological images is an extremely challenging task. However, the obtained results are promising, and it is possible to state that automatic diagnostic methods are competitive to manual ones.
Publisher:
Zielona Góra: Uniwersytet Zielonogórski
Resource Identifier:
DOI:
Pages:
Source:
AMCS, volume 31, number 1 (2021) ; click here to follow the link
Language:
License CC BY 4.0:
Rights:
Biblioteka Uniwersytetu Zielonogórskiego
Object collections:
- Repository > Faculties > Faculty of Computer, Electrical and Control Engineering
- Repository > Types of work > Articles
- Repository > Scientific journals and UZ publishing series > International Journal of Applied Mathematics and Computer Science (AMCS) > International Journal of Applied Mathematics and Computer Science (AMCS) (2021)
Last modified:
Jul 18, 2025
In our library since:
Jul 18, 2025
Number of object content hits:
12
All available object's versions:
https://zbc.uz.zgora.pl/repozytorium/publication/101331
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 31, number 1 (2021)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 31, number 2 (2021)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 31, number 3 (2021)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 31, number 4 (2021)
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| Edition name | Date |
|---|---|
| Breast cancer nuclei segmentation and classification based on a deep learning approach | Jul 18, 2025 |
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