Abstract
Based on deep learning technology, this paper proposes a two-stage colorectal image feature mining and fast recognition model to achieve fully automatic medical image pathology discrimination. Drawing on the ideas of multi-factor Meta-regression analysis widely used in the medical field and the model aggregation framework based on Bayesian prior probability theory, a prognostic model of colorectal tumors suitable for various situations and scenarios is constructed. And using a combination of public data sets and real data sets, design two sets of experiments to verify these models from different angles. The algorithm was used to select one, four, and five related features from three sequences to construct three sets of prediction models. The application of the six algorithms failed to obtain a better predictive model (AUC value range 0.439 ~ 0.640). The algorithm (AUC value 0.750± 0.137) and the algorithm (AUC value 0.764± 0.128) can be used to obtain models with better predictive performance, and the four models are less effective (AUC value< 0.7). In the joint model, the algorithm (AUC value 0.742 ± 0.101) and the algorithm (AUC value 0.718± 0.069) can also be used to obtain a model with better prediction performance. Image-based imaging histology tags can be used as a non-invasive auxiliary tool for preoperative evaluation of histological grading of CRAC, and are expected to be applied in clinical practice to assist in the development of individualized treatment plans.