Open AccessData Analysis for Risk Prediction of Cervical Cancer Metastasis and Recurrence Based on DCNN-RF
Author(s) -
Xiaohong Joe Zhou,
Weihong Li,
Zhicheng Wen
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1813/1/012033
Subject(s) - metastasis , pathological , cervical cancer , medicine , generalization , eosin , cancer , artificial intelligence , oncology , computer science , pathology , mathematics , staining , mathematical analysis
In allusion to the problem of the low survival rate of cervical cancer metastasis and recurrence, combining the advantages of deep learning, a hybrid DCNN-RF method based on patched pathological image was proposed to predict the risk of metastasis and recurrence in cervical cancer patients. In order to improve the generalization ability of the model, according to the features, predicting result could be obtained from random forest, and the integration result was invoked as the result of haematoxylin and eosin pathological whole-slide images (WSI). The experimental results show that the model yielded an accuracy of 90.32% for prediction based on sliding window in cross-validation, and 0.83 AUC in the WSI.