Computer assisted recognition of breast cancer in biopsy images via fusion of nucleus-guided deep convolutional features
| Autor: | Kalpana George, Praveen Sankaran, Paul Joseph K |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
medicine.medical_specialty
Support Vector Machine Computer science Biopsy Feature extraction Breast Neoplasms Health Informatics Image processing Convolutional neural network 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine Breast cancer medicine Humans medicine.diagnostic_test Computers business.industry Deep learning Cancer Pattern recognition medicine.disease Computer Science Applications Support vector machine Female Histopathology Neural Networks Computer Artificial intelligence business Classifier (UML) 030217 neurology & neurosurgery Software |
| Zdroj: | Computer Methods and Programs in Biomedicine. 194:105531 |
| ISSN: | 0169-2607 |
| DOI: | 10.1016/j.cmpb.2020.105531 |
| Popis: | Background and objective: Breast cancer is a commonly detected cancer among women, resulting in a high number of cancer-related mortality. Biopsy performed by pathologists is the final confirmation procedure for breast cancer diagnosis. Computer-aided diagnosis systems can support the pathologist for better diagnosis and also in reducing subjective errors. Methods: In the automation of breast cancer analysis, feature extraction is a challenging task due to the structural diversity of the breast tissue images. Here, we propose a nucleus feature extraction methodology using a convolutional neural network (CNN), ‘NucDeep’, for automated breast cancer detection. Non-overlapping nuclei patches detected from the images enable the design of a low complexity CNN for feature extraction. A feature fusion approach with support vector machine classifier (FF + SVM) is used to classify breast tumor images based on the extracted CNN features. The feature fusion method transforms the local nuclei features into a compact image-level feature, thus improving the classifier performance. A patch class probability based decision scheme (NucDeep + SVM + PD) for image-level classification is also introduced in this work. Results: The proposed framework is evaluated on the publicly available BreaKHis dataset by conducting 5 random trials with 70-30 train-test data split, achieving average image level recognition rate of 96.66 ± 0.77%, 100% specificity and 96.21% sensitivity. Conclusion: It was found that the proposed NucDeep + FF + SVM model outperforms several recent existing methods and reveals a comparable state of the art performance even with low training complexity. As an effective and inexpensive model, the classification of biopsy images for breast tumor diagnosis introduced in this research will thus help to develop a reliable support tool for pathologists. |
| Databáze: | OpenAIRE |
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