Common colon disease classification method based on deep neural network and auxiliary system

Abstract

The invention relates to the field of image recognition of computer vision and the field of artificial intelligence, and provides a common colon disease classification method based on a deep neural network and an auxiliary system, and the auxiliary system comprises a model training module and an auxiliary diagnosis module. The method comprises the steps of obtaining a multi-classification data setand a binary classification data set; training based on the binary classification data set to obtain a normal abnormal deep neural network classification model; training based on the multi-classification data set to obtain a lesion type deep neural network classification model; fusing the two models to obtain a deep neural network disease classification model of the colonoscope image, in the model, firstly inputting the image in the test set into a normal abnormal deep neural network classification model, if the image is judged to be normal, directly outputting a judgment result, and if the image is judged to be abnormal, inputting into a lesion type deep neural network classification model, further determining the lesion type, and outputting a judgment result.

Description

technical field [0001] The invention relates to the field of image recognition of computer vision and the field of artificial intelligence, in particular to a method for classifying common colonic diseases based on a deep neural network and an auxiliary system. Background technique [0002] Colon cancer is a common malignant tumor of the digestive tract, and its incidence rate ranks third among gastrointestinal tumors. In terms of clinical manifestations, colon cancer usually has no symptoms in the early stage, and symptoms will not appear until the middle and late stages. Colonoscopy is the most intuitive and effective way to find colon cancer. In addition to malignant tumor lesions, there are many types of lesions in the colon, such as inflammation and polyps. Most colon cancers are evolved from polyps. Therefore, in the process of colonoscopy, the timely and accurate detection of intestinal lesions and their correct classification will have an important impact on the ev...

AI Technical Summary

Problems solved by technology

[0003] At present, there are still many defects in the manual inspection method of colonoscopy in hospitals. For example, the judgment of lesion location and type depends entirely on the doctor's operation level, knowledge level and clinical experience, and the subjective influence factors are relatively large; at the same time, the inspection of colonoscopy is time-consuming. It is labor-intensive. It usually takes half an hour for a patient, and up to an hour for a complicated case. Long-term repetitive work has high requirements on the patience and carefulness of the doctor's operation.

These factors have led to a high rate of missed detection and false detection during the inspection process.

Images