A New Deep Convolutional Neural Network Model for Automated Breast Cancer Detection

Xujuan Zhou; Yuefeng Li; Raj Gururajan; Ghazal Bargshady; Xiaohui Tao; Revathi Venkataraman; Prabal D. Barua; Srinivas Kondalsamy-Chennakesavan

doi:10.1109/BESC51023.2020.9348322

A New Deep Convolutional Neural Network Model for Automated Breast Cancer Detection

Xujuan Zhou
, Yuefeng Li
, Raj Gururajan
, Ghazal Bargshady
, Xiaohui Tao
, Revathi Venkataraman
, Prabal D. Barua
, Srinivas Kondalsamy-Chennakesavan

Research output: A Conference proceeding or a Chapter in Book › Conference contribution › peer-review

18 Citations (Scopus)

Abstract

Breast cancer is reported as one of most common malignancy amongst women in the world. Early detection of this cancer is critical to clinical and epidemiologic for aiding in informing subsequent treatments. This study investigates automated breast cancer prediction using deep learning techniques. A new 19-layer deep convolutional neural network (CNN) model for detecting the benign breast tumors from malignant cancers was proposed and implemented. The experiments on BreaKHis dataset was conducted and K-fold Cross Validation technique are used for the model evaluation. The proposed 19-layer deep CNN based classifiers compared with conventional machine learning classifier, namely Support Vector Machine (SVM) and a state-of-the-art deep learning model, namely GoogLeNet in terms of Accuracy, Area under the Receiver Operating Characteristic (ROC) Curve (AUC), the Classification Mean Absolute Error (MAE), Mean Squared Error (MSE) metrics. The results demonstrate that the proposed new model outperformed the other classifiers. The proposed model achieved an accuracy, AUC, MAE and MSE of 84.5%, 85.7%, 0.082, and 0.043, respectively.

Original language	English
Title of host publication	Proceedings of 2020 7th IEEE International Conference on Behavioural and Social Computing, BESC 2020
Place of Publication	United States
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	1-4
Number of pages	4
ISBN (Electronic)	9781728186054
DOIs	https://doi.org/10.1109/BESC51023.2020.9348322
Publication status	Published - 5 Nov 2020
Externally published	Yes
Event	7th IEEE International Conference on Behavioural and Social Computing, BESC 2020 - Bournemouth, United Kingdom Duration: 5 Nov 2020 → 7 Nov 2020

Publication series

Name	Proceedings of 2020 7th IEEE International Conference on Behavioural and Social Computing, BESC 2020

Conference

Conference	7th IEEE International Conference on Behavioural and Social Computing, BESC 2020
Country/Territory	United Kingdom
City	Bournemouth
Period	5/11/20 → 7/11/20

UN SDGs

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

SDG 3 Good Health and Well-being

Access to Document

10.1109/BESC51023.2020.9348322

Cite this

Zhou, X., Li, Y., Gururajan, R., Bargshady, G., Tao, X., Venkataraman, R., Barua, P. D., & Kondalsamy-Chennakesavan, S. (2020). A New Deep Convolutional Neural Network Model for Automated Breast Cancer Detection. In Proceedings of 2020 7th IEEE International Conference on Behavioural and Social Computing, BESC 2020 (pp. 1-4). Article 9348322 (Proceedings of 2020 7th IEEE International Conference on Behavioural and Social Computing, BESC 2020). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/BESC51023.2020.9348322

Zhou, Xujuan ; Li, Yuefeng ; Gururajan, Raj et al. / A New Deep Convolutional Neural Network Model for Automated Breast Cancer Detection. Proceedings of 2020 7th IEEE International Conference on Behavioural and Social Computing, BESC 2020. United States : IEEE, Institute of Electrical and Electronics Engineers, 2020. pp. 1-4 (Proceedings of 2020 7th IEEE International Conference on Behavioural and Social Computing, BESC 2020).

@inproceedings{8770fb010f08418dad1ef97920e4a0e0,

title = "A New Deep Convolutional Neural Network Model for Automated Breast Cancer Detection",

abstract = "Breast cancer is reported as one of most common malignancy amongst women in the world. Early detection of this cancer is critical to clinical and epidemiologic for aiding in informing subsequent treatments. This study investigates automated breast cancer prediction using deep learning techniques. A new 19-layer deep convolutional neural network (CNN) model for detecting the benign breast tumors from malignant cancers was proposed and implemented. The experiments on BreaKHis dataset was conducted and K-fold Cross Validation technique are used for the model evaluation. The proposed 19-layer deep CNN based classifiers compared with conventional machine learning classifier, namely Support Vector Machine (SVM) and a state-of-the-art deep learning model, namely GoogLeNet in terms of Accuracy, Area under the Receiver Operating Characteristic (ROC) Curve (AUC), the Classification Mean Absolute Error (MAE), Mean Squared Error (MSE) metrics. The results demonstrate that the proposed new model outperformed the other classifiers. The proposed model achieved an accuracy, AUC, MAE and MSE of 84.5\%, 85.7\%, 0.082, and 0.043, respectively.",

keywords = "breast cancer, computer vision, deep convolutional network, deep learning, machine learning",

author = "Xujuan Zhou and Yuefeng Li and Raj Gururajan and Ghazal Bargshady and Xiaohui Tao and Revathi Venkataraman and Barua, \{Prabal D.\} and Srinivas Kondalsamy-Chennakesavan",

note = "Funding Information: ACKNOWLEDGMENT This research work is partially supported by the Commonwealth Innovation Connections Grant, Australia (No. RC54960). Publisher Copyright: {\textcopyright} 2020 IEEE.; 7th IEEE International Conference on Behavioural and Social Computing, BESC 2020 ; Conference date: 05-11-2020 Through 07-11-2020",

year = "2020",

month = nov,

day = "5",

doi = "10.1109/BESC51023.2020.9348322",

language = "English",

series = "Proceedings of 2020 7th IEEE International Conference on Behavioural and Social Computing, BESC 2020",

publisher = "IEEE, Institute of Electrical and Electronics Engineers",

pages = "1--4",

booktitle = "Proceedings of 2020 7th IEEE International Conference on Behavioural and Social Computing, BESC 2020",

address = "United States",

}

Zhou, X, Li, Y, Gururajan, R, Bargshady, G, Tao, X, Venkataraman, R, Barua, PD & Kondalsamy-Chennakesavan, S 2020, A New Deep Convolutional Neural Network Model for Automated Breast Cancer Detection. in Proceedings of 2020 7th IEEE International Conference on Behavioural and Social Computing, BESC 2020., 9348322, Proceedings of 2020 7th IEEE International Conference on Behavioural and Social Computing, BESC 2020, IEEE, Institute of Electrical and Electronics Engineers, United States, pp. 1-4, 7th IEEE International Conference on Behavioural and Social Computing, BESC 2020, Bournemouth, United Kingdom, 5/11/20. https://doi.org/10.1109/BESC51023.2020.9348322

A New Deep Convolutional Neural Network Model for Automated Breast Cancer Detection. / Zhou, Xujuan; Li, Yuefeng; Gururajan, Raj et al.
Proceedings of 2020 7th IEEE International Conference on Behavioural and Social Computing, BESC 2020. United States: IEEE, Institute of Electrical and Electronics Engineers, 2020. p. 1-4 9348322 (Proceedings of 2020 7th IEEE International Conference on Behavioural and Social Computing, BESC 2020).

Research output: A Conference proceeding or a Chapter in Book › Conference contribution › peer-review

TY - GEN

T1 - A New Deep Convolutional Neural Network Model for Automated Breast Cancer Detection

AU - Zhou, Xujuan

AU - Li, Yuefeng

AU - Gururajan, Raj

AU - Bargshady, Ghazal

AU - Tao, Xiaohui

AU - Venkataraman, Revathi

AU - Barua, Prabal D.

AU - Kondalsamy-Chennakesavan, Srinivas

PY - 2020/11/5

Y1 - 2020/11/5

N2 - Breast cancer is reported as one of most common malignancy amongst women in the world. Early detection of this cancer is critical to clinical and epidemiologic for aiding in informing subsequent treatments. This study investigates automated breast cancer prediction using deep learning techniques. A new 19-layer deep convolutional neural network (CNN) model for detecting the benign breast tumors from malignant cancers was proposed and implemented. The experiments on BreaKHis dataset was conducted and K-fold Cross Validation technique are used for the model evaluation. The proposed 19-layer deep CNN based classifiers compared with conventional machine learning classifier, namely Support Vector Machine (SVM) and a state-of-the-art deep learning model, namely GoogLeNet in terms of Accuracy, Area under the Receiver Operating Characteristic (ROC) Curve (AUC), the Classification Mean Absolute Error (MAE), Mean Squared Error (MSE) metrics. The results demonstrate that the proposed new model outperformed the other classifiers. The proposed model achieved an accuracy, AUC, MAE and MSE of 84.5%, 85.7%, 0.082, and 0.043, respectively.

AB - Breast cancer is reported as one of most common malignancy amongst women in the world. Early detection of this cancer is critical to clinical and epidemiologic for aiding in informing subsequent treatments. This study investigates automated breast cancer prediction using deep learning techniques. A new 19-layer deep convolutional neural network (CNN) model for detecting the benign breast tumors from malignant cancers was proposed and implemented. The experiments on BreaKHis dataset was conducted and K-fold Cross Validation technique are used for the model evaluation. The proposed 19-layer deep CNN based classifiers compared with conventional machine learning classifier, namely Support Vector Machine (SVM) and a state-of-the-art deep learning model, namely GoogLeNet in terms of Accuracy, Area under the Receiver Operating Characteristic (ROC) Curve (AUC), the Classification Mean Absolute Error (MAE), Mean Squared Error (MSE) metrics. The results demonstrate that the proposed new model outperformed the other classifiers. The proposed model achieved an accuracy, AUC, MAE and MSE of 84.5%, 85.7%, 0.082, and 0.043, respectively.

KW - breast cancer

KW - computer vision

KW - deep convolutional network

KW - deep learning

KW - machine learning

UR - https://www.scopus.com/pages/publications/85101660490

UR - http://besc-conf.org/2020/index.html

U2 - 10.1109/BESC51023.2020.9348322

DO - 10.1109/BESC51023.2020.9348322

M3 - Conference contribution

AN - SCOPUS:85101660490

T3 - Proceedings of 2020 7th IEEE International Conference on Behavioural and Social Computing, BESC 2020

SP - 1

EP - 4

BT - Proceedings of 2020 7th IEEE International Conference on Behavioural and Social Computing, BESC 2020

PB - IEEE, Institute of Electrical and Electronics Engineers

CY - United States

T2 - 7th IEEE International Conference on Behavioural and Social Computing, BESC 2020

Y2 - 5 November 2020 through 7 November 2020

ER -

Zhou X, Li Y, Gururajan R, Bargshady G, Tao X, Venkataraman R et al. A New Deep Convolutional Neural Network Model for Automated Breast Cancer Detection. In Proceedings of 2020 7th IEEE International Conference on Behavioural and Social Computing, BESC 2020. United States: IEEE, Institute of Electrical and Electronics Engineers. 2020. p. 1-4. 9348322. (Proceedings of 2020 7th IEEE International Conference on Behavioural and Social Computing, BESC 2020). doi: 10.1109/BESC51023.2020.9348322

A New Deep Convolutional Neural Network Model for Automated Breast Cancer Detection

Abstract

Publication series

Conference

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this