research-article

Effects of Image Augmentation and Dual-layer Transfer Machine Learning Architecture on Tumor Classification

Authors:

Stephen LemosAuthors Info & Claims

ICCPR '19: Proceedings of the 2019 8th International Conference on Computing and Pattern Recognition

Pages 282 - 287

https://doi.org/10.1145/3373509.3373584

Published: 25 March 2020 Publication History

Get Access

Abstract

Breast tumor (BT) is the second most common health problem for women. Traditional diagnosis methods can be very labor-intensive and time-consuming with the risk of making a wrong diagnosis. Computer vision and imaging processing techniques using machine learning (ML) methods are emerging to aide in clinical diagnosis. Some machine learning methods have yielded an accuracy of 85% using a single-layer classifier. In this study Inception-V3, a two-layer classifier of transfer machine learning tool was used for image processing with enhancement technologies and for the classification of breast tumor histopathological types. Results showed that image augmentation with dual-layer transfer machine learning algorithms yielded an accuracy of 95.6% in identification of breast tumor pathologic types, which was higher than previously reported methods in the literature. Different image preprocessing methods, dataset preparing methods, and classifier architectures were also studied to identify the optimal algorithm. Results showed that multiple-layer processing algorithms using color images, instead of black and white images, yielded a better accuracy in histopathological type classification.

References

[1]

Spanhol, F. A., Oliveira, L. S., Petitjean, C. and Heutte, L. A Dataset for Breast Cancer Histopathological Image Classification. Ieee T Bio-Med Eng, 63, 7 (Jul 2016), 1455--1462.

Google Scholar

[2]

Kowal, M., Filipczuk, P., Obuchowicz, A., Korbicz, J. and Monczak, R. Computer-aided diagnosis of breast cancer based on fine needle biopsy microscopic images. Comput Biol Med, 43, 10 (Oct 1--2013), 1563--1572.

Digital Library

Google Scholar

[3]

George, Y. M., Zayed, H. H., Roushdy, M. I. and Elbagoury, B. M. Remote Computer-Aided Breast Cancer Detection and Diagnosis System Based on Cytological Images. Ieee Syst J, 8, 3 (Sep 2014), 949--964.

Google Scholar

[4]

Zhang, Y. G., Zhang, B. L., Coenen, F. and Lu, W. J. Breast cancer diagnosis from biopsy images with highly reliable random subspace classifier ensembles. Mach Vision Appl, 24, 7 (Oct 2013), 1405--1420.

Digital Library

Google Scholar

[5]

Gupta, V. and Bhavsar, A. Breast Cancer Histopathological Image Classification: Is Magnification Important? Ieee Comput Soc Conf (2017), 769--776.

Crossref

Google Scholar

[6]

Spanhol, F. A., Cavalin, P. R., Oliveira, L. S., Petitjean, C. and Heutte, L. Deep Features for Breast Cancer Histopathological Image Classification. Ieee Sys Man Cybern (2017), 1868--1873.

Google Scholar

[7]

Wei, F. M., Zhang, J. P., Chu, Y. and Yang, J. FSFP: Transfer Learning From Long Texts to the Short. Appl Math Inform Sci, 8, 4 (Jul 2014), 2033--2040.

Google Scholar

[8]

Alom, M. Z., Yakopcic, C., Nasrin, M. S., Taha, T. M. and Asari, V. K. Breast Cancer Classification from Histopathological Images with Inception Recurrent Residual Convolutional Neural Network. Journal of Digital Imaging (2019/02/12--2019).

Google Scholar

[9]

Araujo, T., Aresta, G., Castro, E., Rouco, J., Aguiar, P., Eloy, C., Polonia, A. and Campilho, A. Classification of breast cancer histology images using Convolutional Neural Networks. Plos One, 12, 6 (Jun 1--2017).

Google Scholar

[10]

Bayramoglu, N., Kannala, J. and Heikkila, J. Deep Learning for Magnification Independent Breast Cancer Histopathology Image Classification. Int C Patt Recog (2016), 2440--2445.

Google Scholar

[11]

Chang, J., Yu, J., Han, T., Chang, H. and Park, E. A method for classifying medical images using transfer learning: A pilot study on histopathology of breast cancer. City, 2017.

Google Scholar

[12]

Xiao, T., Liu, L., Li, K., Qin, W. J., Yu, S. D. and Li, Z. C. Comparison of Transferred Deep Neural Networks in Ultrasonic Breast Masses Discrimination. Biomed Res Int (2018).

Google Scholar

[13]

Spanhol, F. A., Oliveira, L. S., Petitjean, C. and Heutte, L. Breast Cancer Histopathological Image Classification using Convolutional Neural Networks. Ieee Ijcnn (2016), 2560--2567.

Google Scholar

[14]

Coudray, N., Ocampo, P. S., Sakellaropoulos, T., Narula, N., Snuderl, M., Fenyo, D., Moreira, A. L., Razavian, N. and Tsirigos, A. Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning. Nat Med, 24, 10 (Oct 2018), 1559--1567.

Crossref

Google Scholar

Cited By

View all

Mohapatra SMuduly SMohanty SRavindra JMohanty S(2022)Evaluation of deep learning models for detecting breast cancer using histopathological mammograms ImagesSustainable Operations and Computers10.1016/j.susoc.2022.06.0013(296-302)Online publication date: 2022
https://doi.org/10.1016/j.susoc.2022.06.001
Mohapatra SMuduly SMohanty SMoharana S(2022)Evaluation of Deep Learning Models for Detecting Breast Cancer Using MammogramsMeta Heuristic Techniques in Software Engineering and Its Applications10.1007/978-3-031-11713-8_11(104-112)Online publication date: 18-Oct-2022
https://doi.org/10.1007/978-3-031-11713-8_11
Cho YJalics ALv DGilbert MDickson KChen DNguyen TJoines HKakos BChen CLemos S(2021)Predicting Rotator Cuff Tear Severity Using Radiographic Images and Machine Learning TechniquesProceedings of the 2021 10th International Conference on Computing and Pattern Recognition10.1145/3497623.3497661(237-241)Online publication date: 15-Oct-2021
https://dl.acm.org/doi/10.1145/3497623.3497661

Index Terms

Effects of Image Augmentation and Dual-layer Transfer Machine Learning Architecture on Tumor Classification
1. Applied computing
  1. Life and medical sciences
    1. Computational biology
      1. Imaging
2. General and reference
  1. Cross-computing tools and techniques
    1. Validation

Recommendations

Transfer Learning Based Classification of Cervical Cancer Immunohistochemistry Images
ISICDM 2019: Proceedings of the Third International Symposium on Image Computing and Digital Medicine

Cervical cancer is the fourth leading cause of cancer-related deaths. It is very important to make the precise diagnosis for the early stage of cervical cancer. In recent years, transfer Learning makes a great breakthrough in the field of machine ...
An experimental study on classification of thyroid histopathology images using transfer learning
Highlights
- An automated computer aided diagnosis system using deep learning is proposed.
- ...
Graphical abstract

Display Omitted

Abstract
CAD systems for histopathology image analysis using machine learning is a well researched subject. Deep learning is playing a major role in advancing this research in the recent years. This paper presents an automated thyroid ...
Optimizing Knowledge Transfer in Sequential Models: Leveraging Residual Connections in Flow Transfer Learning for Lung Cancer Classification
ICVGIP '23: Proceedings of the Fourteenth Indian Conference on Computer Vision, Graphics and Image Processing

Lung cancer is a prevalent and life-threatening disease characterized by abnormal cell growth in lung tissue. Early detection and accurate classification of lung cancer are crucial for timely treatment and improved patient outcomes. Our work proposes, a ...

Comments

Information & Contributors

Information

Published In

ICCPR '19: Proceedings of the 2019 8th International Conference on Computing and Pattern Recognition

October 2019

522 pages

ISBN:9781450376570

DOI:10.1145/3373509

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 ACM 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]

In-Cooperation

Hebei University of Technology
Beijing University of Technology

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 March 2020

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

ICCPR '19

ICCPR '19: 2019 8th International Conference on Computing and Pattern Recognition

October 23 - 25, 2019

Beijing, China

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

3
Total Citations
View Citations
92
Total Downloads

Downloads (Last 12 months)2
Downloads (Last 6 weeks)0

Reflects downloads up to 10 Oct 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Mohapatra SMuduly SMohanty SRavindra JMohanty S(2022)Evaluation of deep learning models for detecting breast cancer using histopathological mammograms ImagesSustainable Operations and Computers10.1016/j.susoc.2022.06.0013(296-302)Online publication date: 2022
https://doi.org/10.1016/j.susoc.2022.06.001
Mohapatra SMuduly SMohanty SMoharana S(2022)Evaluation of Deep Learning Models for Detecting Breast Cancer Using MammogramsMeta Heuristic Techniques in Software Engineering and Its Applications10.1007/978-3-031-11713-8_11(104-112)Online publication date: 18-Oct-2022
https://doi.org/10.1007/978-3-031-11713-8_11
Cho YJalics ALv DGilbert MDickson KChen DNguyen TJoines HKakos BChen CLemos S(2021)Predicting Rotator Cuff Tear Severity Using Radiographic Images and Machine Learning TechniquesProceedings of the 2021 10th International Conference on Computing and Pattern Recognition10.1145/3497623.3497661(237-241)Online publication date: 15-Oct-2021
https://dl.acm.org/doi/10.1145/3497623.3497661

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

Transfer Learning Based Classification of Cervical Cancer Immunohistochemistry Images

An experimental study on classification of thyroid histopathology images using transfer learning

Optimizing Knowledge Transfer in Sequential Models: Leveraging Residual Connections in Flow Transfer Learning for Lung Cancer Classification

Comments

Published In

In-Cooperation

Publisher

Publication History

Permissions

Check for updates

Author Tags

Qualifiers

Conference

Other Metrics

Article Metrics

Other Metrics

Cited By

Login options

Full Access

PDF

eReader

Abstract

References

Cited By

Index Terms

Recommendations

Transfer Learning Based Classification of Cervical Cancer Immunohistochemistry Images

An experimental study on classification of thyroid histopathology images using transfer learning

Optimizing Knowledge Transfer in Sequential Models: Leveraging Residual Connections in Flow Transfer Learning for Lung Cancer Classification

Comments

Information

Published In

In-Cooperation

Publisher

Publication History

Permissions

Check for updates

Author Tags

Qualifiers

Conference

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

Get Access

Login options

Full Access

View options

PDF

eReader

Figures

Other

Share

Share this Publication link

Share on social media

Affiliations