Journal of ICT Research and Applications ITB Bandung Vol. 15 No. 2 2021
Automated Detection and Classification of Breast Cancer Nuclei with Deep Convolutional Neural Network
Dublin Core
Title
Journal of ICT Research and Applications ITB Bandung Vol. 15 No. 2 2021
Automated Detection and Classification of Breast Cancer Nuclei with Deep Convolutional Neural Network
Automated Detection and Classification of Breast Cancer Nuclei with Deep Convolutional Neural Network
Subject
breast cancer; classification; deep convolutional neural network; Dice
score; ResNet.
score; ResNet.
Description
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.
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.
Creator
Shanmugham Balasundaram, Revathi Balasundaram, Ganesan Rasuthevar, Christeena Joseph, Annie Grace Vimala, Nanmaran Rajendiran& Baskaran Kaliyamurthy
Source
DOI: 10.5614/itbj.ict.res.appl.2021.15.2.3
Publisher
IRCS-ITB
Date
07 Juli 2021
Contributor
Sri Wahyuni
Rights
ISSN: 2337-5787
Format
PDF
Language
English
Type
Text
Coverage
Journal of ICT Research and Applications ITB Bandung Vol. 15 No. 2 2021
Files
Collection
Citation
Shanmugham Balasundaram, Revathi Balasundaram, Ganesan Rasuthevar, Christeena Joseph, Annie Grace Vimala, Nanmaran Rajendiran& Baskaran Kaliyamurthy, “Journal of ICT Research and Applications ITB Bandung Vol. 15 No. 2 2021
Automated Detection and Classification of Breast Cancer Nuclei with Deep Convolutional Neural Network,” Repository Horizon University Indonesia, accessed October 31, 2025, https://repository.horizon.ac.id/items/show/3421.
Automated Detection and Classification of Breast Cancer Nuclei with Deep Convolutional Neural Network,” Repository Horizon University Indonesia, accessed October 31, 2025, https://repository.horizon.ac.id/items/show/3421.