A Survey of Convolutional Neural Network in Breast Cancer

• Published in: Computer modeling in engineering & sciences Vol. 136; no. 3; pp. 2127 - 2172
• Main Authors: Zhu, Ziquan, Wang, Shui-Hua, Zhang, Yu-Dong
• Format: Journal Article
• Language: English
• Published: United States Tech Science Press 01.01.2023
• Subjects: Artificial neural networks; Breast cancer; Datasets; Diagnosis; Health services; Life expectancy; Neural networks; deep learning; Breast cancer; convolutional neural network; review; image modalities
• ISSN: 1526-1506; 1526-1492; 1526-1506
• DOI: 10.32604/cmes.2023.025484

For people all over the world, cancer is one of the most feared diseases. Cancer is one of the major obstacles to improving life expectancy in countries around the world and one of the biggest causes of death before the age of 70 in 112 countries. Among all kinds of cancers, breast cancer is the most common cancer for women. The data showed that female breast cancer had become one of the most common cancers. A large number of clinical trials have proved that if breast cancer is diagnosed at an early stage, it could give patients more treatment options and improve the treatment effect and survival ability. Based on this situation, there are many diagnostic methods for breast cancer, such as computer-aided diagnosis (CAD). We complete a comprehensive review of the diagnosis of breast cancer based on the convolutional neural network (CNN) after reviewing a sea of recent papers. Firstly, we introduce several different imaging modalities. The structure of CNN is given in the second part. After that, we introduce some public breast cancer data sets. Then, we divide the diagnosis of breast cancer into three different tasks: 1. classification; 2. detection; 3. segmentation. Although this diagnosis with CNN has achieved great success, there are still some limitations. (i) There are too few good data sets. A good public breast cancer dataset needs to involve many aspects, such as professional medical knowledge, privacy issues, financial issues, dataset size, and so on. (ii) When the data set is too large, the CNN-based model needs a sea of computation and time to complete the diagnosis. (iii) It is easy to cause overfitting when using small data sets.