A Survey of Convolutional Neural Network in Breast Cancer

Abstract

Problems 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. Aims 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). Methods 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. Conclusion 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.

Breast cancer: A comparative review for breast cancer detection using machine learning techniques

“…The primary strength of deep recurrent neural network (RNN) model 43‐45 for BC prediction using the Keras–Tuner optimization technique. This study evaluates the optimized deep RNN (Figure 5) with five common ML methods: DT, SVM, RF, NB, and K ‐NN 46 . Both traditional ML and DL methods 42 are used in the proposed system for BC prediction.…”

“…with five common ML methods: DT, SVM, RF, NB, and K-NN. 46 Both traditional ML and DL methods 42 are used in the proposed system for BC prediction. Furthermore, the timing of reproductive experiences could also be the cause.…”

An exploration of ventricle regions segmentation and multiclass disease detection using cardiac MRI

“…42 According to the statistics, female breast cancer has risen to the top of the list of cancers. Zhu et al 43 analyzed a wide range of recent papers and provide a thorough assessment of the convolutional neural network-based diagnosis of breast cancer. They start with a review of several kinds of imaging techniques.…”

Active contour and texture features hybrid model for breast cancer detection from ultrasonic images

“…Breast cancer is one of the leading causes of mortality among women worldwide. According to the World Health Organization (WHO), breast lesion affects more than 1.5 million women worldwide every year 1–3 . It is one of the most well‐known types of cancer 4 .…”

“…According to the World Health Organization (WHO), breast lesion affects more than 1.5 million women worldwide every year. [1][2][3] It is one of the most well-known types of cancer. 4 It accounts for nearly a quarter of all female deaths.…”