A system and method for learning through a neural network to input images of biological tissue in different multi-color models by using a learned neural network to diagnose a predetermined disease (e.g., prostate cancer) is disclosed. According to one aspect of the present invention, as a disease diagnosis system implemented in a system including a processor and a storage device storing a neural network and using a living body image, i.e., a slide, and the neural network, the system comprises a pre-processing module for pre-processing the slide, wherein the pre-processing module generates, foreach image block formed by dividing the slide by a predetermined size, first to K-th image information (wherein K is an integer of 2 or more) corresponding to the image block, and the i-th image information (i is any integer of 1<= i <= K) is composed of at least one channel value corresponding to the i-th color model (color model), and the first color model to the K-th color model are differentcolor models; and an image block neural network that, for each of the segmented image blocks, receives the first to N-th image information corresponding to the image block as an input layer, and outputs an image block level diagnosis result indicating whether or not a disease exists in the image block.
DISEASE DIAGNOSIS SYSTEM AND METHOD USING MULTIPLE COLOR MODELS AND NEURAL NETWORK