Breast Cancer Detection Based on Image Denoising in Multiple Modes
- DOI
- 10.2991/978-94-6463-040-4_84How to use a DOI?
- Keywords
- Image denoising; Breast cancer classification; Convolutional neural network
- Abstract
Breast cancer is cancer that develops from breast tissue, and it is the leading type of cancer in women. Convolutional neural network (CNN) is a very effective auxiliary method for medical image detection and classification as well as denoising, which is very important for diagnosis and analysis of medical images. In this study, DenseNet was used for breast cancer image classification and REDNet and a PRIDNet was sued for image denoising. By comparing the accuracy of different input with different noise level and denoising model, this study showed that denoising can remove the redundant information of images with noise and improve the accuracy of classification and higher Peak Signal to Noise Ratio (PSNR) and Structural Similarity (SSIM) would lead to a higher classification accuracy. The model performed better on the images with similar noise to the noise of the training image.
- Copyright
- © 2023 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Zheng Yin AU - Shijie Pang AU - Yi Yang PY - 2022 DA - 2022/12/27 TI - Breast Cancer Detection Based on Image Denoising in Multiple Modes BT - Proceedings of the 2022 3rd International Conference on Artificial Intelligence and Education (IC-ICAIE 2022) PB - Atlantis Press SP - 554 EP - 559 SN - 2589-4900 UR - https://doi.org/10.2991/978-94-6463-040-4_84 DO - 10.2991/978-94-6463-040-4_84 ID - Yin2022 ER -