METHOD FOR DEEP LEARNING BASED INTESTINE DISEASE SUBSIDIARY DIAGNOSIS

摘要

An embodiment of the present invention relates to a method for deep learning based large intestine disease subsidiary diagnosis. The method comprises the steps of: (A) creating a diagnosis model by performing deep learning on large intestine diseases using learning data; (B) inputting an image for diagnosis; and (C) making a diagnosis on the image for diagnosis with respect to large intestine diseases based on the diagnosis model. The step (A) comprises the steps of: (A1) inputting, as first learning data, a plurality of first large intestine images and large intestine information for each of the first large intestine images; (A2) creating a first classification model classifying the first large intestine images as normal, benign, or malignant through deep learning using the first learning data as an input; (A3) inputting, as second learning data, second large intestine images in which the first large intestine images are classified as normal,benign or malignant according to the first classification model, and disease information on each of the second large intestine images; and (A4) creating a second classification model classifying the second large intestine images by disease type through deep learning using the second learning data as an input. In the step (C), applying the first classification model and the second classification model as a diagnosis model is desirable.;COPYRIGHT KIPO 2020