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

Zhou, Xujuan and Li, Yuefeng and Gururajan, Raj ORCID: https://orcid.org/0000-0002-5919-0174 and Bargshady, Ghazal ORCID: https://orcid.org/0000-0002-2557-7928 and Tao, Xiaohui ORCID: https://orcid.org/0000-0002-0020-077X and Venkataraman, Revathi and Barua, Prabal D. and Kondalsamy-Chennakesavan, Srinivas (2020) A New Deep Convolutional Neural Network Model for Automated Breast Cancer Detection. In: 7th International Conference on Behavioural and Social Computing (BESC 2020), 5-7 Nov 2020, Bournemouth, United Kingdom.


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.


Statistics for USQ ePrint 40135
Statistics for this ePrint Item
Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Faculty/School / Institute/Centre: Historic - Faculty of Business, Education, Law and Arts - School of Management and Enterprise (1 Jul 2013 - 17 Jan 2021)
Faculty/School / Institute/Centre: Historic - Faculty of Business, Education, Law and Arts - School of Management and Enterprise (1 Jul 2013 - 17 Jan 2021)
Date Deposited: 24 Nov 2020 05:47
Last Modified: 04 Jan 2021 01:06
Uncontrolled Keywords: breast cancer; deep convolutional network, machine learning, deep learning, computer vision
Fields of Research (2008): 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining
Fields of Research (2020): 46 INFORMATION AND COMPUTING SCIENCES > 4611 Machine learning > 461103 Deep learning
Socio-Economic Objectives (2008): E Expanding Knowledge > 97 Expanding Knowledge > 970108 Expanding Knowledge in the Information and Computing Sciences
Socio-Economic Objectives (2020): 28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280115 Expanding knowledge in the information and computing sciences
URI: http://eprints.usq.edu.au/id/eprint/40135

Actions (login required)

View Item Archive Repository Staff Only