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

Kowal, Marek ; Skobel, Marcin ; Gramacki, Artur ; Korbicz, Józef

International Journal of Applied Mathematics and Computer Science, Tome 31 (2021) no. 1, pp. 85-106.

Résumé

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

Détail

Keywords: breast cancer, nuclei segmentation, image processing
Mots-clés : nowotwór piersi, segmentacja jądra, przetwarzanie obrazu

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