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Paper details
Number 1 - March 2021
Volume 31 - 2021
Breast cancer nuclei segmentation and classification based on a deep learning approach
Marek Kowal, Marcin Skobel, Artur Gramacki, Józef Korbicz
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.
Keywords
breast cancer, nuclei segmentation, classification, image processing