An Efficient Method of Histological Cell Image Detection Based on Spatial Information Convolution Neural Network
Qi Qiang, Hong Wang, Peng Likang · 2019
As an important research direction in the field of medical images, histopathological cell image detection has been widely used in computer-aided diagnosis, biological research fields. With the rise of deep learning, neural network is applied to medical image analysis, which can realize the automatic detection and classification of histological cell images. In order to solve the problem that the output of the existing neural network is affected by spatial information factors in its topological domain, on the basis of the traditional convolution neural network. Combined with the spatial position information, an improved convolution neural network model for histological cell image detection is proposed. Taking the traditional convolution neural network as the carrier, the convolution neural network model based on spatial information is constructed, which makes the model has the ability to fuse spatial information and eigenvector. Histopathological cell images were preprocessed by color deconvolution. Finally, a model verification experiment based on colorectal cancer image dataset is designed. The model proposed in this paper shows better performance than the state-of-the-art methods in four different categories (more than 20000 experimental images): the experimental accuracy is 75.8%, and the recall rate is 82.3%. F1 reached 80.1%.