research-article

Breast mass lesion classification in mammograms by transfer learning

Authors:

Yaoqin XieAuthors Info & Claims

ICBCB '17: Proceedings of the 5th International Conference on Bioinformatics and Computational Biology

January 2017

Pages 59 - 62

https://doi.org/10.1145/3035012.3035022

Published: 06 January 2017 Publication History

Abstract

Automatic classification of breast mass lesions in mammographic images remains an unsolved problem. This paper explored the technique of transfer learning to tackle this problem. It utilized the convolutional neural network (CNN) of GoogLeNet and AlexNet pre-trained on a large-scale visual database. The performance was evaluated a new dataset in terms of the area under the receiver operating characteristic curves (AUC). Results demonstrate that GoogLeNet (AUC=0.88) outperforms AlexNet (AUC=0.83) and other state-of-the-art traditional approaches in breast cancer diagnosis. The technique of transfer learning not only overcomes the unsatisfactory performance of traditional approaches, but also breaks the obstacle of limited samples for building deep CNNs.

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Cited By

Kukreja SSabharwal MKatiyar AGill D(2024)Automated Classification of Cancer using Heuristic Class Topper Optimization based Naïve Bayes ClassifierSN Computer Science10.1007/s42979-023-02586-35:2Online publication date: 10-Feb-2024
https://dl.acm.org/doi/10.1007/s42979-023-02586-3
Hanis TRuhaiyem NArifin WHaron JWan Abdul Rahman WAbdullah RMusa K(2023)Developing a Supplementary Diagnostic Tool for Breast Cancer Risk Estimation Using Ensemble Transfer LearningDiagnostics10.3390/diagnostics1310178013:10(1780)Online publication date: 18-May-2023
https://doi.org/10.3390/diagnostics13101780
Ansar WRaza B(2023)Breast Cancer Segmentation in Mammogram Using Artificial Intelligence and Image Processing: A Systematic ReviewCurrent Chinese Science10.2174/22102981026662204061218143:1(3-22)Online publication date: Mar-2023
https://doi.org/10.2174/2210298102666220406121814
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Index Terms

Breast mass lesion classification in mammograms by transfer learning
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks
        Biometrics

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cover image ACM Other conferences

ICBCB '17: Proceedings of the 5th International Conference on Bioinformatics and Computational Biology

January 2017

72 pages

ISBN:9781450348270

DOI:10.1145/3035012

Conference Chair:
David Zhang
Hong Kong Polytechnic University, Hong Kong

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 January 2017

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Guangdong province program
Guangdong Innovative Research Team Program
Shenzhen Fundamental Research Program

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ICBCB '17

ICBCB '17: 2017 5th International Conference on Bioinformatics and Computational Biology

January 6 - 8, 2017

Hong Kong, Hong Kong

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Cited By

Kukreja SSabharwal MKatiyar AGill D(2024)Automated Classification of Cancer using Heuristic Class Topper Optimization based Naïve Bayes ClassifierSN Computer Science10.1007/s42979-023-02586-35:2Online publication date: 10-Feb-2024
https://dl.acm.org/doi/10.1007/s42979-023-02586-3
Hanis TRuhaiyem NArifin WHaron JWan Abdul Rahman WAbdullah RMusa K(2023)Developing a Supplementary Diagnostic Tool for Breast Cancer Risk Estimation Using Ensemble Transfer LearningDiagnostics10.3390/diagnostics1310178013:10(1780)Online publication date: 18-May-2023
https://doi.org/10.3390/diagnostics13101780
Ansar WRaza B(2023)Breast Cancer Segmentation in Mammogram Using Artificial Intelligence and Image Processing: A Systematic ReviewCurrent Chinese Science10.2174/22102981026662204061218143:1(3-22)Online publication date: Mar-2023
https://doi.org/10.2174/2210298102666220406121814
Arun Kumar SSasikala S(2023)Review on Deep Learning-Based CAD Systems for Breast Cancer DiagnosisTechnology in Cancer Research & Treatment10.1177/1533033823117797722(153303382311779)Online publication date: 6-Jun-2023
https://doi.org/10.1177/15330338231177977
Yan RLiu YLiu YWang LZhao RBai YGui Z(2023)Image Denoising for Low-Dose CT via Convolutional Dictionary Learning and Neural NetworkIEEE Transactions on Computational Imaging10.1109/TCI.2023.32415469(83-93)Online publication date: 2023
https://doi.org/10.1109/TCI.2023.3241546
Ben Ammar MAyechi FKsantini RMahjoubi H(2023)Pre-Trained Deep Convolutional Neural Network Architectures for Breast Cancer Diagnosis in Mammography: Current State-Of-The-Art2023 International Conference on Innovations in Intelligent Systems and Applications (INISTA)10.1109/INISTA59065.2023.10310612(1-7)Online publication date: 20-Sep-2023
https://doi.org/10.1109/INISTA59065.2023.10310612
Sarada CDattatreya VLakshmi K(2023)Deep Learning based Breast Image Classification Study for Cancer Detection2023 IEEE International Conference on Integrated Circuits and Communication Systems (ICICACS)10.1109/ICICACS57338.2023.10100206(01-08)Online publication date: 24-Feb-2023
https://doi.org/10.1109/ICICACS57338.2023.10100206
Saini VKhurana MChalla R(2023)Advancements in Breast Cancer Detection: A Comprehensive Review of Deep Learning Techniques for Mammogram Analysis2023 1st International Conference on Circuits, Power and Intelligent Systems (CCPIS)10.1109/CCPIS59145.2023.10291244(1-6)Online publication date: 1-Sep-2023
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Saber AHussien AAwad WMahmoud AAllakany A(2023)Adapting the pre-trained convolutional neural networks to improve the anomaly detection and classification in mammographic imagesScientific Reports10.1038/s41598-023-41633-013:1Online publication date: 9-Sep-2023
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Yu XRen ZGuttery DZhang Y(2023)DF-dRVFL: A novel deep feature based classifier for breast mass classificationMultimedia Tools and Applications10.1007/s11042-023-15864-283:5(14393-14422)Online publication date: 11-Jul-2023
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