Image Based Tumor Cells Identification Using Convolutional Neural Network and Auto Encoders

Abstract

The convolutional neural network (CNN) and other neural networks (NNs) provide promising tools for robotized characterization of tumor cells. However, the tumor growth areas in ultrasound images are normally obscure, with uncertain edges. It is not acceptable to prepare ultrasound images straightforwardly with the CNN. To solve the problem, this paper puts forward a faster region-convolutional neural network (R-CNN) to identify tumor cells with the aid of auto encoders. Taking two fully-connected layers with dropout and ReLU enactments as the base, the proposed faster R-CNN adopts 3D convolutional and max pooling layers, enabling the user to extract features from potential tumor growth areas. In addition, the thin and deep layers of the network were connected to facilitate the identification of blurry or small tumor growth areas. Experimental results show that the proposed faster R-CNN with auto encoders outperformed traditional data mining and artificial intelligence (AI) methods in prediction accuracy of tumor cells.