Detecting cervical intraepithelial neoplasia using polarimetry parameters and multichannel convolutional neural network

摘要

Early diagnosis and fast screening of cervical cancer is the key to prognosis of treatment and patient survival. Polarimetry technique with high sensitivity to microstructures and low requirement for resolution is promising at facilitating the fast screening and quantitative diagnosis. In this study, we apply the Mueller matrix microscope and multichannel convolutional neural network for the detection of human cervical intraepithelial neoplasia (CIN) samples from normal samples. The Mueller matrix polar decomposition and transformation parameters, rotation invariant parameters, and Mueller matrix symmetry-related parameters of the cervical tissues in epithelial region and at different stages are calculated and analyzed. For detection of early cervical lesions, the selection method of polarimetry parameters based on statistical features and multichannel convolutional neural network (CNN) for classification are proposed. To illustrate, we select the input parameters of CNN models from all commonly used polarimetry parameters according to the amount of information which are evaluated by the mean value, standard deviation, and information entropy of all pixels in 2D parameters images of the training samples. In multichannel CNN classification, each selected parameter is treated as an input of a channel. The proper multichannel CNN models learn deep features from the selected polarimetry parameters of training samples and show good performance for detecting CIN samples under a low-resolution system.