Breast cancer identification based on RS and BP neural networks

Jiaxin Li; Weibing Feng

doi:10.1117/12.2646303

23 August 2022 Breast cancer identification based on RS and BP neural networks

Jiaxin Li, Weibing Feng

Proceedings Volume 12330, International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2022); 123301W (2022) https://doi.org/10.1117/12.2646303
Event: International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2022), 2022, Huzhou, China

Abstract

Breast cancer has become the most cancerous cancer in the world. Along with the increase in the number of confirmed breast cancer patients, the early diagnosis of breast cancer is extremely important. Machine learning methods have a wide range of applications in breast cancer tumor diagnosis, of which artificial neural networks are an effective tool for computer-assisted breast cancer diagnosis, and error backpropagation (BP) neural networks are the most widely used models. This paper provides a diagnostic decision based on the combination of rough set (RS) and BP neural network, using the rough set genetic reduction algorithm as a feature selection tool, and using the minimum number of features and the number of identifiable features in the optimal distinction matrix as the fitness function to achieve feature reduction, remove redundant attributes, further improve the diagnostic accuracy of breast cancer on the basis of a single neural network classification model, and select the five feature factors with the greatest influence on breast cancer. Experiments with breast cancer datasets in the UCI database show that this method effectively improves the accuracy of classified diagnosis compared with the original BP neural network.

Citation Download Citation

Jiaxin Li and Weibing Feng "Breast cancer identification based on RS and BP neural networks", Proc. SPIE 12330, International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2022), 123301W (23 August 2022); https://doi.org/10.1117/12.2646303

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available