DEEP LEARNING ARCHITECTURE SYSTEM FOR AUTOMATIC INTERPRETATION OF FUNDUS IMAGE AND METHOD FOR AUTOMATIC INTERPRETATION OF FUNDUS IMAGE THEREOF

摘要

The present invention relates to an algorithm for automatic fundus image reading, and a deep for automatic fundus image reading that can minimize the amount of data required for learning by training and reading artificial intelligence in a manner similar to that an ophthalmologist acquires medical knowledge. It's about learning architecture. The deep learning architecture system for automatic fundus image reading according to the present invention is composed of a plurality of convolutional layers that perform feature extraction of fundus images and one pooling layer that performs subsampling to reduce the amount of computation. A trunk module 100 that bundles a common part in a plurality of convolutional neural network (CNN) architectures in which at least one extraction layer set is arranged in series; a branch module 200 that is provided in plurality, generates each architecture in the trunk module 100, receives the output of the trunk module 100, identifies a lesion in the fundus image, and diagnoses a corresponding disease name; a section 110 that is an architecture connecting the branch module 200 and the trunk module 100 of any one of the plurality of branch modules 200; a root layer 120 connecting the trunk module 100 and the branch module 200 by transferring the output of a specific layer of the trunk module 100 to the branch module 200; and a final diagnosis unit 300 that integrates the diagnosed data from the branch module 200 provided in plurality to determine and output the final disease name.