Breast cancer histopathological image classification using convolutional neural networks with small SE-ResNet module

By Yun Jiang, Li Chen, Hai Zhang, Xiao Xiao. PLoS ONE, 2019

340 citations recorded by the source corpus.

Although successful detection of malignant tumors from histopathological images largely depends on the long-term experience of radiologists, experts sometimes disagree with their decisions. Computer-aided diagnosis provides a second option for image diagnosis, which can improve the reliability of experts’ decision-making. Automatic and precision classification for breast cancer histopathological image is of great importance in clinical application for identifying malignant tumors from histopathological images. Advanced convolution neural network technology has achieved great success in natural image classification, and it has been used widely in biomedical image processing. In this paper, we design a novel convolutional neural network, which includes a convolutional layer, small SE-ResNet module, and fully connected layer. We propose a small SE-ResNet module which is an improvement on the combination of residual module and Squeeze-and-Excitation block, and achieves the similar performance with fewer parameters. In addition, we propose a new learning rate scheduler which can get excellent performance without complicatedly fine-tuning the learning rate. We use our model for the automatic classification of breast cancer histology images (BreakHis dataset) into benign and malignant and eight subtypes. The results show that our model achieves the accuracy between 98.87% and 99.34% for the binary classification and achieve the accuracy between 90.66% and 93.81% for the multi-class classification.

Subfields: Artificial Intelligence, Biomedical Engineering, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/4e8a53da0e76b29b33a209f8df79902b2b1f77f7

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Yan Sun
2. Yanhui Guo
3. Fan Liu
4. Shouheng Peng
5. Shuhao Wang
6. Xiangchun Li
7. Zewen Li
8. Meng Yang

In Biomedical Engineering

1. Yanhui Guo
2. Abdulkadir Sengür
3. Varun Bajaj
4. Erkan Deniz
5. Zehra Kadiroglu
6. Ümit Budak

In Radiology, Nuclear Medicine and Imaging

1. Yan Sun
2. Shuhao Wang
3. Xiangchun Li
4. Meng Yang
5. Yahui Jiang
6. Abdulkadir Sengür

Citations in the corpus

• A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects (2020, methodology): “[124] proposed a method based on SE-ResNet modules to classify breast cancer tissues.”
• Transfer learning based histopathologic image classification for breast cancer detection (2018, methodology): “In [19], researchers applied end-to-end CNN approach and in [6], the authors used various classifiers such as neural networks (NN), quadratic discriminant analysis (QDA), SVM and random forest (RF) for breast cancer detection in breast histo-pathological images.”
• Emerging role of deep learning‐based artificial intelligence in tumor pathology (2020, methodology): “Similarly, DL-based AL were performed for multi-categorization of colorectal polyp [34], ovarian cancer [35], thyroid tumor [36], breast tumor [37], and cervical squamous cell carcinoma [38].”