Classification Of Breast Cancer Histology Images Using ALEXNET

Wajahat Nawaz; Sagheer Ahmed; Ali Tahir; Hassan Aqeel Khan

doi:10.1007/978-3-319-93000-8_99

Classification Of Breast Cancer Histology Images Using ALEXNET

Wajahat Nawaz^*
, Sagheer Ahmed
, Ali Tahir
, Hassan Aqeel Khan

^*Corresponding author for this work

National University of Sciences and Technology Pakistan

Research output: Chapter in Book/Published conference output › Conference publication

97 Citations (Scopus)

Abstract

Training a deep convolutional neural network from scratch requires massive amount of data and significant computational power. However, to collect a large amount of data in medical field is costly and difficult, but this can be solved by some clever tricks such as mirroring, rotating and fine tuning pre-trained neural networks. In this paper, we fine tune a deep convolutional neural network (ALEXNET) by changing and inserting input layer convolutional layers and fully connected layer. Experimental results show that our method achieves a patch and image-wise accuracy of 75.73% and 81.25% respectively on the validation set and image-wise accuracy of 57% on the ICIAR-2018 breast cancer challenge hidden test set.

Original language	English
Title of host publication	Image Analysis and Recognition - 15th International Conference, ICIAR 2018, Proceedings
Editors	Bart ter Haar Romeny, Fakhri Karray, Aurelio Campilho
Publisher	Springer
Pages	869-876
Number of pages	8
ISBN (Print)	9783319929996
DOIs	https://doi.org/10.1007/978-3-319-93000-8_99
Publication status	Published - 6 Jun 2018
Event	15th International Conference on Image Analysis and Recognition, ICIAR 2018 - Povoa de Varzim, Portugal Duration: 27 Jun 2018 → 29 Jun 2018

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10882 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	15th International Conference on Image Analysis and Recognition, ICIAR 2018
Country/Territory	Portugal
City	Povoa de Varzim
Period	27/06/18 → 29/06/18

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Carcinoma cancer
Convolution neural network
Deep learning
Pathologists
Transfer learning

Access to Document

10.1007/978-3-319-93000-8_99Licence: Unspecified

Cite this

Nawaz, W., Ahmed, S., Tahir, A., & Khan, H. A. (2018). Classification Of Breast Cancer Histology Images Using ALEXNET. In B. ter Haar Romeny, F. Karray, & A. Campilho (Eds.), Image Analysis and Recognition - 15th International Conference, ICIAR 2018, Proceedings (pp. 869-876). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10882 LNCS). Springer. https://doi.org/10.1007/978-3-319-93000-8_99

Nawaz, Wajahat ; Ahmed, Sagheer ; Tahir, Ali et al. / Classification Of Breast Cancer Histology Images Using ALEXNET. Image Analysis and Recognition - 15th International Conference, ICIAR 2018, Proceedings. editor / Bart ter Haar Romeny ; Fakhri Karray ; Aurelio Campilho. Springer, 2018. pp. 869-876 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{6268846d2a0b4c1da547d18f09cfe64a,

title = "Classification Of Breast Cancer Histology Images Using ALEXNET",

abstract = "Training a deep convolutional neural network from scratch requires massive amount of data and significant computational power. However, to collect a large amount of data in medical field is costly and difficult, but this can be solved by some clever tricks such as mirroring, rotating and fine tuning pre-trained neural networks. In this paper, we fine tune a deep convolutional neural network (ALEXNET) by changing and inserting input layer convolutional layers and fully connected layer. Experimental results show that our method achieves a patch and image-wise accuracy of 75.73\% and 81.25\% respectively on the validation set and image-wise accuracy of 57\% on the ICIAR-2018 breast cancer challenge hidden test set.",

keywords = "Carcinoma cancer, Convolution neural network, Deep learning, Pathologists, Transfer learning",

author = "Wajahat Nawaz and Sagheer Ahmed and Ali Tahir and Khan, \{Hassan Aqeel\}",

year = "2018",

month = jun,

day = "6",

doi = "10.1007/978-3-319-93000-8\_99",

language = "English",

isbn = "9783319929996",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer",

pages = "869--876",

editor = "\{ter Haar Romeny\}, Bart and Fakhri Karray and Aurelio Campilho",

booktitle = "Image Analysis and Recognition - 15th International Conference, ICIAR 2018, Proceedings",

address = "Germany",

note = "15th International Conference on Image Analysis and Recognition, ICIAR 2018 ; Conference date: 27-06-2018 Through 29-06-2018",

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Nawaz, W, Ahmed, S, Tahir, A & Khan, HA 2018, Classification Of Breast Cancer Histology Images Using ALEXNET. in B ter Haar Romeny, F Karray & A Campilho (eds), Image Analysis and Recognition - 15th International Conference, ICIAR 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10882 LNCS, Springer, pp. 869-876, 15th International Conference on Image Analysis and Recognition, ICIAR 2018, Povoa de Varzim, Portugal, 27/06/18. https://doi.org/10.1007/978-3-319-93000-8_99

Classification Of Breast Cancer Histology Images Using ALEXNET. / Nawaz, Wajahat; Ahmed, Sagheer; Tahir, Ali et al.
Image Analysis and Recognition - 15th International Conference, ICIAR 2018, Proceedings. ed. / Bart ter Haar Romeny; Fakhri Karray; Aurelio Campilho. Springer, 2018. p. 869-876 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10882 LNCS).

Research output: Chapter in Book/Published conference output › Conference publication

TY - GEN

T1 - Classification Of Breast Cancer Histology Images Using ALEXNET

AU - Nawaz, Wajahat

AU - Ahmed, Sagheer

AU - Tahir, Ali

AU - Khan, Hassan Aqeel

PY - 2018/6/6

Y1 - 2018/6/6

N2 - Training a deep convolutional neural network from scratch requires massive amount of data and significant computational power. However, to collect a large amount of data in medical field is costly and difficult, but this can be solved by some clever tricks such as mirroring, rotating and fine tuning pre-trained neural networks. In this paper, we fine tune a deep convolutional neural network (ALEXNET) by changing and inserting input layer convolutional layers and fully connected layer. Experimental results show that our method achieves a patch and image-wise accuracy of 75.73% and 81.25% respectively on the validation set and image-wise accuracy of 57% on the ICIAR-2018 breast cancer challenge hidden test set.

AB - Training a deep convolutional neural network from scratch requires massive amount of data and significant computational power. However, to collect a large amount of data in medical field is costly and difficult, but this can be solved by some clever tricks such as mirroring, rotating and fine tuning pre-trained neural networks. In this paper, we fine tune a deep convolutional neural network (ALEXNET) by changing and inserting input layer convolutional layers and fully connected layer. Experimental results show that our method achieves a patch and image-wise accuracy of 75.73% and 81.25% respectively on the validation set and image-wise accuracy of 57% on the ICIAR-2018 breast cancer challenge hidden test set.

KW - Carcinoma cancer

KW - Convolution neural network

KW - Deep learning

KW - Pathologists

KW - Transfer learning

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DO - 10.1007/978-3-319-93000-8_99

M3 - Conference publication

AN - SCOPUS:85049473466

SN - 9783319929996

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 869

EP - 876

BT - Image Analysis and Recognition - 15th International Conference, ICIAR 2018, Proceedings

A2 - ter Haar Romeny, Bart

A2 - Karray, Fakhri

A2 - Campilho, Aurelio

PB - Springer

T2 - 15th International Conference on Image Analysis and Recognition, ICIAR 2018

Y2 - 27 June 2018 through 29 June 2018

ER -

Nawaz W, Ahmed S, Tahir A, Khan HA. Classification Of Breast Cancer Histology Images Using ALEXNET. In ter Haar Romeny B, Karray F, Campilho A, editors, Image Analysis and Recognition - 15th International Conference, ICIAR 2018, Proceedings. Springer. 2018. p. 869-876. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-93000-8_99

Classification Of Breast Cancer Histology Images Using ALEXNET

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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