An SVM approach towards breast cancer classification from H&E-stained histopathology images based on integrated features

Breast cancer is one among the most frequent reasons of women's death worldwide. Nowadays, healthcare informatics is mainly focussing on the classification of breast cancer images, due to the lethal nature of this cancer. There are chances of inter- and intra-observer variability that may lead to misdiagnosis in the detection of cancer. This study proposed an automatic breast cancer classification system that uses support vector machine (SVM) classifier based on integrated features (texture, geometrical, and color). The University of California Santa Barbara (UCSB) dataset and BreakHis dataset, which are available in public domain, were used. A classification comparison module which involves SVM, k-nearest neighbor (k-NN), random forest (RF), and artificial neural network (ANN) was also proposed to determine the classifier that best suits for the application of breast cancer detection from histopathology images. The performance of these classifiers was analyzed against metrics like accuracy, specificity, sensitivity, balanced accuracy, and F-score. Results showed that among the classifiers, the SVM classifier performed better with a test accuracy of approximately 90% on both the datasets. Additionally, the significance of the proposed integrated SVM model was statistically analyzed against other classifier models.

Automated summary

Breast cancer is a leading cause of death in women globally, prompting a focus on healthcare informatics for the classification of breast cancer images. An automatic classification system utilizing SVM classifier and integrated features was proposed and analyzed for accuracy using UCSB and BreakHis datasets. SVM classifier demonstrated higher accuracy compared to other models for breast cancer detection.