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
N1 - Funding Information:
ACKNOWLEDGMENT This research work is partially supported by the Commonwealth Innovation Connections Grant, Australia (No. RC54960).
Publisher Copyright:
© 2020 IEEE.
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 - http://www.scopus.com/inward/record.url?scp=85101660490&partnerID=8YFLogxK
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 -
