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Automated Detection and Classification of Breast Cancer Nuclei with Deep Convolutional Neural Network

Journal of ICT Research and Applications (JICTRA) · Vol. 15 No. 2 (2021) · pp. 139–151

DOI
10.5614/itbj.ict.res.appl.2021.15.2.3
ISSN
2338-5499, 2337-5787
Published
2021-10-07
Publisher
DRI - ITB
Language
eng

Abstract

Heterogeneous regions present in tissue with respect to cancer cells are of various types. This study aimed to analyze and classify the morphological features of the nucleus and cytoplasm regions of tumor cells. This tissue morphology study was established through invasive ductal breast cancer histopathology images accessed from the Databiox public dataset. Automatic detection and classification was carried out by means of the computer analytical tool of deep learning algorithm. Residual blocks with short skip were employed with hidden layers of preserved spatial information. A ResNet-based convolutional neural network was adapted to perform end-to-end segmentation of breast cancer nuclei. Nuclei regions were identified through color and tubular structure morphological features. Based on the segmented and extracted images, classification of benign and malignant breast cancer cells was done to identify tumors. The results indicated that the proposed method could successfully segment and classify breast tumors with an average Dice score of 90.68%, sensitivity = 98.64, specificity = 98.68, and accuracy = 98.82.

Keywords

Collaboration network of the authors

7 authors · 21 co-authorship links · 5 across different organisations

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  • Saveetha University
  • E.G.S. Pillay Engineering College
  • SRM Institute of Science and Technology
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
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Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journals.itb.ac.id/index.php/jictra/oai, first harvested 26 Okt 2026, last seen 1 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Automated Detection and Classification of Breast Cancer Nuclei with Deep Convolutional Neural Network</dc:title>
	<dc:creator>Balasundaram, Shanmugham</dc:creator>
	<dc:creator>Balasundaram, Revathi</dc:creator>
	<dc:creator>Rasuthevar, Ganesan</dc:creator>
	<dc:creator>Joseph, Christeena</dc:creator>
	<dc:creator>Vimala , Annie Grace</dc:creator>
	<dc:creator>Rajendiran, Nanmaran</dc:creator>
	<dc:creator>Kaliyamurthy, Baskaran</dc:creator>
	<dc:subject xml:lang="en-US">breast cancer</dc:subject>
	<dc:subject xml:lang="en-US">classification</dc:subject>
	<dc:subject xml:lang="en-US">deep convolutional neural network</dc:subject>
	<dc:subject xml:lang="en-US">Dice score</dc:subject>
	<dc:subject xml:lang="en-US">ResNet</dc:subject>
	<dc:description xml:lang="en-US">Heterogeneous regions present in tissue with respect to cancer cells are of various types. This study aimed to analyze and classify the morphological features of the nucleus and cytoplasm regions of tumor cells. This tissue morphology study was established through invasive ductal breast cancer histopathology images accessed from the Databiox public dataset. Automatic detection and classification was carried out by means of the computer analytical tool of deep learning algorithm. Residual blocks with short skip were employed with hidden layers of preserved spatial information. A ResNet-based convolutional neural network was adapted to perform end-to-end segmentation of breast cancer nuclei. Nuclei regions were identified through color and tubular structure morphological features. Based on the segmented and extracted images, classification of benign and malignant breast cancer cells was done to identify tumors. The results indicated that the proposed method could successfully segment and classify breast tumors with an average Dice score of 90.68%, sensitivity = 98.64, specificity = 98.68, and accuracy = 98.82.</dc:description>
	<dc:publisher xml:lang="en-US">DRI - ITB</dc:publisher>
	<dc:date>2021-10-07</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:type xml:lang="en-US">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journals.itb.ac.id/index.php/jictra/article/view/15799</dc:identifier>
	<dc:identifier>10.5614/itbj.ict.res.appl.2021.15.2.3</dc:identifier>
	<dc:source xml:lang="en-US">Journal of ICT Research and Applications; Vol. 15 No. 2 (2021); 139-151</dc:source>
	<dc:source>2338-5499</dc:source>
	<dc:source>2337-5787</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journals.itb.ac.id/index.php/jictra/article/view/15799/5416</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2021 Journal of ICT Research and Applications</dc:rights>
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