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Breast cancer histopathological image classification using convolutional neural networks with small SE-ResNet module
- Source :
- PLoS ONE, PLoS ONE, Vol 14, Iss 3, p e0214587 (2019)
- Publication Year :
- 2019
- Publisher :
- Public Library of Science, 2019.
-
Abstract
- 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.
- Subjects :
- Computer science
Papillary Carcinomas
02 engineering and technology
Pathology and Laboratory Medicine
Convolutional neural network
Machine Learning
0302 clinical medicine
Mathematical and Statistical Techniques
Breast Tumors
0202 electrical engineering, electronic engineering, information engineering
Medicine and Health Sciences
Image Processing, Computer-Assisted
Multidisciplinary
Artificial neural network
Contextual image classification
Molecular Imaging
Binary classification
Oncology
030220 oncology & carcinogenesis
Medicine
020201 artificial intelligence & image processing
Research Article
Computer and Information Sciences
Neural Networks
Science
Histopathology
Image processing
Breast Neoplasms
Research and Analysis Methods
Carcinomas
Residual neural network
03 medical and health sciences
Breast cancer
Artificial Intelligence
Diagnostic Medicine
Support Vector Machines
Breast Cancer
medicine
Cancer Detection and Diagnosis
Humans
Block (data storage)
business.industry
Cancers and Neoplasms
Biology and Life Sciences
Pattern recognition
medicine.disease
Convolution
Support vector machine
Anatomical Pathology
Artificial intelligence
Neural Networks, Computer
business
Mathematical Functions
Neuroscience
Subjects
Details
- Language :
- English
- ISSN :
- 19326203
- Volume :
- 14
- Issue :
- 3
- Database :
- OpenAIRE
- Journal :
- PLoS ONE
- Accession number :
- edsair.doi.dedup.....509a81eef383f4989e8fba01854b5944