Breast cancer image classification via multi-network features and dual-network orthogonal low-rank learning

Histopathological image analysis is an important technique for early diagnosis and detection of breast cancer in clinical practice. However, it has limited efficiency and thus the detection of breast cancer is still an open issue in medical image analysis. To improve the early diagnostic accuracy of...

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Main Authors: Wang, Yongjun, Lei, Baiying, Elazab, Ahmed, Tan, Ee-Leng, Wang, Wei, Huang, Fanglin, Gong, Xuehao, Wang, Tianfu
Other Authors: School of Electrical and Electronic Engineering
Format: Article
Language:English
Published: 2021
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Online Access:https://hdl.handle.net/10356/145755
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1457552021-01-07T02:43:56Z Breast cancer image classification via multi-network features and dual-network orthogonal low-rank learning Wang, Yongjun Lei, Baiying Elazab, Ahmed Tan, Ee-Leng Wang, Wei Huang, Fanglin Gong, Xuehao Wang, Tianfu School of Electrical and Electronic Engineering Engineering::Electrical and electronic engineering Breast Cancer Image Classification Deep Convolutional Neural Network Histopathological image analysis is an important technique for early diagnosis and detection of breast cancer in clinical practice. However, it has limited efficiency and thus the detection of breast cancer is still an open issue in medical image analysis. To improve the early diagnostic accuracy of breast cancer and reduce the workload of doctors, we devise a classification framework based on histology images by combining deep learning with machine learning methodologies in this paper. Specifically, we devise a multi-network feature extraction model by using pre-trained deep convolution neural networks (DCNNs), develop an effective feature dimension reduction method and train an ensemble support vector machine (E-SVM). First, we preprocess the histological images via scale transformation and color enhancement methods. Second, the multi-network features are extracted by using four pre-trained DCNNs (e.g., DenseNet-121, ResNet-50, multi-level InceptionV3, and multi-level VGG-16). Third, a feature selection method via dual-network orthogonal low-rank learning (DOLL) is further developed for performance boosting and overfitting alleviation. Finally, an E-SVM is trained via fused features and voting strategy to perform the classification task, which classifies the images into four classes (i.e., benign, in situ carcinomas, invasive carcinomas, and normal). We evaluate the proposed method on the public ICIAR 2018 Challenge dataset of histology images of breast cancer and achieve a high classification accuracy of 97.70%. Experimental results show that our method can achieve quite promising performance and outperform state-of-the-art methods. Published version 2021-01-07T02:43:56Z 2021-01-07T02:43:56Z 2020 Journal Article Wang, Y., Lei, B., Elazab, A., Tan, E.-L., Wang, W., Huang, F., . . . Wang, T. (2020). Breast cancer image classification via multi-network features and dual-network orthogonal low-rank learning. IEEE Access, 8, 27779-27792. doi:10.1109/ACCESS.2020.2964276 2169-3536 https://hdl.handle.net/10356/145755 10.1109/ACCESS.2020.2964276 8 27779 27792 en IEEE Access © 2020 IEEE. This journal is 100% open access, which means that all content is freely available without charge to users or their institutions. All articles accepted after 12 June 2019 are published under a CC BY 4.0 license, and the author retains copyright. Users are allowed to read, download, copy, distribute, print, search, or link to the full texts of the articles, or use them for any other lawful purpose, as long as proper attribution is given. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Electrical and electronic engineering
Breast Cancer Image Classification
Deep Convolutional Neural Network
spellingShingle Engineering::Electrical and electronic engineering
Breast Cancer Image Classification
Deep Convolutional Neural Network
Wang, Yongjun
Lei, Baiying
Elazab, Ahmed
Tan, Ee-Leng
Wang, Wei
Huang, Fanglin
Gong, Xuehao
Wang, Tianfu
Breast cancer image classification via multi-network features and dual-network orthogonal low-rank learning
description Histopathological image analysis is an important technique for early diagnosis and detection of breast cancer in clinical practice. However, it has limited efficiency and thus the detection of breast cancer is still an open issue in medical image analysis. To improve the early diagnostic accuracy of breast cancer and reduce the workload of doctors, we devise a classification framework based on histology images by combining deep learning with machine learning methodologies in this paper. Specifically, we devise a multi-network feature extraction model by using pre-trained deep convolution neural networks (DCNNs), develop an effective feature dimension reduction method and train an ensemble support vector machine (E-SVM). First, we preprocess the histological images via scale transformation and color enhancement methods. Second, the multi-network features are extracted by using four pre-trained DCNNs (e.g., DenseNet-121, ResNet-50, multi-level InceptionV3, and multi-level VGG-16). Third, a feature selection method via dual-network orthogonal low-rank learning (DOLL) is further developed for performance boosting and overfitting alleviation. Finally, an E-SVM is trained via fused features and voting strategy to perform the classification task, which classifies the images into four classes (i.e., benign, in situ carcinomas, invasive carcinomas, and normal). We evaluate the proposed method on the public ICIAR 2018 Challenge dataset of histology images of breast cancer and achieve a high classification accuracy of 97.70%. Experimental results show that our method can achieve quite promising performance and outperform state-of-the-art methods.
author2 School of Electrical and Electronic Engineering
author_facet School of Electrical and Electronic Engineering
Wang, Yongjun
Lei, Baiying
Elazab, Ahmed
Tan, Ee-Leng
Wang, Wei
Huang, Fanglin
Gong, Xuehao
Wang, Tianfu
format Article
author Wang, Yongjun
Lei, Baiying
Elazab, Ahmed
Tan, Ee-Leng
Wang, Wei
Huang, Fanglin
Gong, Xuehao
Wang, Tianfu
author_sort Wang, Yongjun
title Breast cancer image classification via multi-network features and dual-network orthogonal low-rank learning
title_short Breast cancer image classification via multi-network features and dual-network orthogonal low-rank learning
title_full Breast cancer image classification via multi-network features and dual-network orthogonal low-rank learning
title_fullStr Breast cancer image classification via multi-network features and dual-network orthogonal low-rank learning
title_full_unstemmed Breast cancer image classification via multi-network features and dual-network orthogonal low-rank learning
title_sort breast cancer image classification via multi-network features and dual-network orthogonal low-rank learning
publishDate 2021
url https://hdl.handle.net/10356/145755
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