Tumor Nuclei Detection in Histopathology Images Using R-CNN
Daria Hlavcheva, Vladyslav Yaloveha, Andrii Podorozhniak, Heorhii Kuchuk · 2020
Breast cancer is the most common in the world and its rates could be increased from 2 million in 2018 to 3 million in 2040, and the death rate from 600 thousand to nearly 1 million per year. Histopathological analysis is used for diagnosis of almost all cancer types. Nowadays histopathological tissue analysis and evaluating the microscopic appearance of a biopsied tissue sample are pro- vided by a pathologist. The paper is devoted to the problem of histopathological analysis automatization using a region-based convolutional neural network (R- CNN). The purpose of the research is to automatizate the tumor nuclei detection in the histopathological images, because detection can be used as qualitative and quantitative analysis. In the research breast cancer histopathological anno- tation and diagnosis dataset is used (BreCaHAD). The classification accuracy for SVM classifier, which uses features, extracted by CNN, is 0.96. The object detection heatmap was built. It is obtained that the average precision for tumor nuclei detection is 0.338. The theory of deep learning neural networks and mathematical statistics methods are used in the research.