Recent trend analysis of convolutional neural network-based breast cancer diagnosis

Mingzhe Liu

doi:10.1117/12.2672660

1 March 2023 Recent trend analysis of convolutional neural network-based breast cancer diagnosis

Mingzhe Liu

Proceedings Volume 12596, International Conference on Mechatronics Engineering and Artificial Intelligence (MEAI 2022); 1259612 (2023) https://doi.org/10.1117/12.2672660
Event: International Conference on Mechatronics Engineering and Artificial Intelligence (MEAI 2022), 2022, Changsha, China

Abstract

One of the most common malignancies worldwide is breast cancer. Early screening and diagnosis are important to the reduction of mortality rates of patients. In order to improve the performance and accuracy of breast cancer image screening, researchers have made significant progress in Computer-aided diagnosis (CAD) systems built on convolutional neural networks (CNN). In this research, several recent CNN models of breast cancer diagnosis are discussed and explained, and multiple public datasets of breast cancer images are introduced. The detailed performances of the models are presented and compared. The limitations and potential improvements of current CNN-based CAD are discussed. Convolution neural network-based CAD are still facing challenges of shortage of public dataset and the problem of implementation in the clinical scenario. Conclusively, using a convolutional neural network to diagnose breast cancer is still at its early stage, and further developments are required to apply convolutional neural network-based cancer diagnosis to clinical practices.

Citation Download Citation

Mingzhe Liu "Recent trend analysis of convolutional neural network-based breast cancer diagnosis", Proc. SPIE 12596, International Conference on Mechatronics Engineering and Artificial Intelligence (MEAI 2022), 1259612 (1 March 2023); https://doi.org/10.1117/12.2672660

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