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All Journal Journal of ICT Research and Applications
Revathi Balasundaram
Dhanalakshmi Srinivasan College of Engineering and Technology, Mamallapuram, Tamilnadu, India
Computer Science & IT

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Automated Detection and Classification of Breast Cancer Nuclei with Deep Convolutional Neural Network Shanmugham Balasundaram; Revathi Balasundaram; Ganesan Rasuthevar; Christeena Joseph; Annie Grace Vimala; Nanmaran Rajendiran; Baskaran Kaliyamurthy
Journal of ICT Research and Applications Vol. 15 No. 2 (2021)
Publisher : LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2021.15.2.3

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.
Co-Authors Annie Grace Vimala Baskaran Kaliyamurthy Christeena Joseph Ganesan Rasuthevar Nanmaran Rajendiran Shanmugham Balasundaram