A disease diagnosis system for diagnosing cancer implemented in a system including a processor and a storage device for storing a neural network, and using a slide, which is a biometric image expressing a biological tissue, and the neural network; the diagnosis system comprising: a patch neural network for generating a patch-level diagnosis result indicating whether a disease exists in each of predetermined patches generated by dividing the slide in a predetermined size; a heatmap generation module for generating a patch-level heatmap image marking a disease patch corresponding to the slide on the basis of the patch-level diagnosis result of each of the plurality of patches included in the slide; a tissue mask generation module for generating a tissue mask image corresponding to the slide on the basis of a hue-saturation-value HSV model corresponding to the slide; and a visualization module for generating a disease diagnosis visualization image corresponding to the slide on the basis of the patch-level heatmap image and the tissue mask image; characterised in that the tissue mask generation module generates a first binarization result by performing image binarization on an S space corresponding to the slide, generates a second binarization result by performing image binarization on a 1-V space corresponding to the slide, and generates a tissue mask image corresponding to the slide on the basis of the first binarization result and the second binarization result; wherein the S space corresponding to the slide is a saturation space of an HSV model corresponding to the slide; wherein the V space corresponding to the slide is a brightness value space of an HSV model corresponding to the slide; wherein the V space is a matrix configured of brightness values of a V channel, and the 1-V space is a space subtracting the values of the V channel from a matrix filled with 1's; wherein the first binarization result and the second binarization result include a binary value corresponding to each pixel of the slide; wherein the binary value is 0 or 1; and wherein for each pixel of the slide, the tissue mask generation module determines a pixel of a tissue mask image corresponding to the pixel as a tissue pixel when the binary value of the first binarization result corresponding to the pixel or the binary value of the second binarization result corresponding to the pixel is 1, and determines the pixel of the tissue mask image corresponding to the pixel as a non-tissue pixel otherwise. a patch neural network for generating a patch-level diagnosis result indicating whether a disease exists in each of predetermined patches generated by dividing the slide in a predetermined size; a heatmap generation module for generating a patch-level heatmap image marking a disease patch corresponding to the slide on the basis of the patch-level diagnosis result of each of the plurality of patches included in the slide; a tissue mask generation module for generating a tissue mask image corresponding to the slide on the basis of a hue-saturation-value HSV model corresponding to the slide; and a visualization module for generating a disease diagnosis visualization image corresponding to the slide on the basis of the patch-level heatmap image and the tissue mask image; characterised in that the tissue mask generation module generates a first binarization result by performing image binarization on an S space corresponding to the slide, generates a second binarization result by performing image binarization on a 1-V space corresponding to the slide, and generates a tissue mask image corresponding to the slide on the basis of the first binarization result and the second binarization result; wherein the S space corresponding to the slide is a saturation space of an HSV model corresponding to the slide; wherein the V space corresponding to the slide is a brightness value space of an HSV model corresponding to the slide; wherein the V space is a matrix configured of brightness values of a V channel, and the 1-V space is a space subtracting the values of the V channel from a matrix filled with 1's; wherein the first binarization result and the second binarization result include a binary value corresponding to each pixel of the slide; wherein the binary value is 0 or 1; and wherein for each pixel of the slide, the tissue mask generation module determines a pixel of a tissue mask image corresponding to the pixel as a tissue pixel when the binary value of the first binarization result corresponding to the pixel or the binary value of the second binarization result corresponding to the pixel is 1, and determines the pixel of the tissue mask image corresponding to the pixel as a non-tissue pixel otherwise.