Cervical Cell Features Based Multi-Stream Convolutional Neural Networks Classification Method

Cervical cell classification is a key problem for computer-aided cervical screening applications. Previous methods mainly focus on designing data-driven methods to learn classification models from labeled dataset, and the domain knowledge of cervical cells problem is seldom explored to further improve the classification performance. To address this problem, we propose a multi-stream convolutional neural network classification algorithm built upon data-driven approach as well as cervical cell domain knowledge. The algorithm uses cell image, nucleus image and artificial designed features based on domain knowledge in the bethesda system(TBS) as input, and extracts multi-stream features through convolutional neural network.Finally, the above three-stream features are fused and then fed to a classification model to give a classification prediction. Experiments show that the classification accuracy of proposed algorithm based on Alexnet is99% on squamous epithelial cells from Herlev cervical cell database, the best reported classification performance on the database up to now. The method proposed achieves the classification accuracy of 85% on our Ideepwise database based on the TBS2014 standard, which gets a gain of 3% compared to the baseline single-stream networks.