APPLICATION OF CONVOLUTIONAL NEURAL NETWORK FOR HISTOPATHOLOGICAL ANALYSIS

Abstract

Among all types of cancer, breast cancer is the most common. In 2017 breast cancer was the fourth rate for death reasons in Ukraine. The paper is devoted to the automatization of histopathological analysis, which can improve the process of cancer stage diagnosis. The purpose of the paper is to research the ability to use convolutional neural networks for classifying biopsy images for cancer diagnosis. The tasks of research are: analyzing cancer statistics in Europe and Ukraine; analyzing usage of Machine Learning in cancer prognosis and diagnosis tasks; preprocessing of BreCaHAD dataset images; developing a convolutional neural network and analyzing results; the building of heatmap. The object of the research is the process of detecting tumors in microscopic biopsy images using Convolutional Neural Network. The subject of the research is the process of classifying healthy and cancerous cells using deep learning neural networks. The scientific novelty of the research is using ConvNet trained on the BreCaHAD dataset for histopathological analysis. The theory of deep learning neural networks and mathematical statistics methods are used. In result it is obtained that the classification accuracy for a convolutional neural network on the test data is 0.935, ConvNet was effectively used for heatmap building.

Keywords

• deep learning
• convolutional neural networks
• breast cancer
• histopathological analysis
• biopsy images
• BreCaHAD