Breast Cancer Histological Images Nuclei Segmentation using Mask Regional Convolutional Neural Network

the breast cancer cases have increased globally, in the last two decades. Analysis of histological images acquired through biopsy of the breast tissues is thought to be the most reliable way to assess if any cells show signs of malignancy. With the advent of deep neural networks, we are now able to...

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Bibliographic Details
Published in2020 IEEE Student Conference on Research and Development (SCOReD) pp. 1 - 6
Main Authors Khan, Fawad Salam, Mohd, Mohd Norzali Haji, Khan, Muhammad Danial, Bagchi, Susama
Format Conference Proceeding
LanguageEnglish
Published IEEE 27.09.2020
Subjects
Breast cancer
Deep learning
Diseases
Histological Images
Image segmentation
Mask Regional Convolutional Network
Neural networks
Nuclei
Reliability
Research and development
Segmentation
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Summary:the breast cancer cases have increased globally, in the last two decades. Analysis of histological images acquired through biopsy of the breast tissues is thought to be the most reliable way to assess if any cells show signs of malignancy. With the advent of deep neural networks, we are now able to diagnose the disease with near perfect accuracy instead of conventional techniques of mammography for breast cancer detection; however, work done in this area considers nuclei segmentation. This work is focussed on extending the applicability of deep learning technology where network is trained to perform segmentation and classify data as one of the four classes: normal, benign, in-situ and invasive. The classification accuracy achieved is 98.16% and the segmentation accuracy calculated using overlap coefficient from mean intersection over union is 86.39% on the ICIAR2018 Breast Cancer dataset.
ISSN:2643-2447
DOI:10.1109/SCOReD50371.2020.9383186