Layer-Wise Tumor Segmentation of Breast Images Using Convolutional Neural Networks

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Author(s): Nishanth Krishnaraj (National Institute of Technology, Tiruchirappalli, India), A. Mary Mekala (Vellore Institute of Technology, Vellore, India), Bhaskar M. (National Institute of Technology, Tiruchirappalli, India), Ruban Nersisson (Vellore Institute of Technology, Vellore, India)and Alex Noel Joseph Raj (Shantou University, China)
Copyright: 2023
Pages: 15
Source title: Research Anthology on Improving Medical Imaging Techniques for Analysis and Intervention
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-6684-7544-7.ch056

Keywords: Knowledge Management and Library Science / Medical Diagnosis and Practice / Medical Imaging / Medical Information Science Reference

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Abstract

Early prediction of cancer type has become very crucial. Breast cancer is common to women and it leads to life threatening. Several imaging techniques have been suggested for timely detection and treatment of breast cancer. More research findings have been done to accurately detect the breast cancer. Automated whole breast ultrasound (AWBUS) is a new breast imaging technology that can render the entire breast anatomy in 3-D volume. The tissue layers in the breast are segmented and the type of lesion in the breast tissue can be identified which is essential for cancer detection. In this chapter, a u-net convolutional neural network architecture is used to implement the segmentation of breast tissues from AWBUS images into the different layers, that is, epidermis, subcutaneous, and muscular layer. The architecture was trained and tested with the AWBUS dataset images. The performance of the proposed scheme was based on accuracy, loss and the F1 score of the neural network that was calculated for each layer of the breast tissue.

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Layer-Wise Tumor Segmentation of Breast Images Using Convolutional Neural Networks

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