The present invention relates to an algorithm for automatic fundus image reading, and to a deep learning architecture for automatic fundus image reading, capable of minimizing the amount of data required for learning by training and reading artificial intelligence in a manner similar to that of an ophthalmologist acquiring medical knowledge. The deep learning architecture system for automatic fundus image reading, according to the present invention, comprises: a trunk module (100) in which common parts are combined into one in a plurality of convolutional neural network (CNN) architectures having at least one serially arranged set of feature extraction layers composed of a plurality of convolution layers that perform fundus image feature extraction and one pooling layer that performs subsampling for reducing computation amount; a plurality of branch modules (200), which generates each architecture in the trunk module (100) so as to receive an output of the trunk module (100), thereby identifying a lesion in the fundus image and diagnosing a corresponding disease name therefor; a section (110), which is an architecture in which any one branch module (200) from among the plurality of branch modules (200) is connected to the trunk module (100); a root layer (120) for connecting the trunk module (100) and the branch module (200) by transmitting the output of a specific layer of the trunk module (100) to the branch module (200); and a final decision unit (300), which integrates diagnosis data from the plurality of branch modules (200) so as to determine and output a final disease name.
DEEP LEARNING ARCHITECTURE SYSTEM FOR AUTOMATIC FUNDUS IMAGE READING AND AUTOMATIC FUNDUS IMAGE READING METHOD USING DEEP LEARNING ARCHITECTURE SYSTEM