Apparatus for diagnosing glaucoma

3:

• The glaucoma diagnosis apparatus of claim 2, wherein the optic disc detection module further configured to detect the optic disc using an image obtained through a polar coordinate transformation on the fundus image.

4:

• The glaucoma diagnosis apparatus of claim 2, wherein the fundus image processor further comprises a preprocessing module configured to resize the fundus image to a predetermined size before the optic disc is detected and horizontally flip the fundus image depending on whether the fundus image is a right fundus image or a left fundus image.

5:

• The glaucoma diagnosis apparatus of claim 2, wherein the image rotation module further configured to set the center of the fundus image as an origin of a coordinate plane, calculate an angle between the center coordinates of the detected optic disc and a horizontal axis (an x-axis) of the coordinate plane, and rotate the fundus image around the origin such that the angle matches a predetermined reference angle.

6:

• The glaucoma diagnosis apparatus of claim 5, wherein the reference angle is any one of 45 degrees or 135 degrees.

7:

• The glaucoma diagnosis apparatus of claim 6, wherein the image rotation module further configured to augment the number of fundus images by flipping the fundus image according to a reference line connecting the origin of the rotated fundus image and the center coordinates of the optic disc or by additionally rotating the rotated fundus image within a predetermined additional rotation range around the origin of the rotated fundus image.

8:

• The glaucoma diagnosis apparatus of claim 2, wherein the first ROI extraction module further configured to extract the ROI such that the center coordinates of the optic disc are spaced one-quarter of a side length from an upper left end or an upper right end of the first ROI.

9:

• The glaucoma diagnosis apparatus of claim 2, wherein the fundus image processor further comprises a histogram matching module configured to, when the fundus image is a test image of a machine learning model, perform histogram matching on the first ROI of the test image on the basis of an average histogram of images included in a learning dataset of the machine learning model.

11:

• The glaucoma diagnosis apparatus of claim 10, wherein the first mask generation module further configured to augment the number of second ROIs by horizontally or vertically moving the center of the second ROI within a predetermined range or by horizontally shifting the image obtained through the polar coordinate transformation on the second ROI within a predetermined range.

12:

• The glaucoma diagnosis apparatus of claim 11, wherein the calculation module further configured to calculate the vCDR using a result of combining the first mask and the second mask generated from an additional image generated by augmenting the same second ROI.

13:

• The glaucoma diagnosis apparatus of claim 1, wherein the determinator further configured to determine whether glaucoma is present in the fundus image through logistic recession analysis on the image classification result and the vCDR calculation result.

14:

• The glaucoma diagnosis apparatus of claim 13, wherein the determinator further configured to perform the logistic regression analysis by using first- to nth-order terms (here, n is a natural number of 2 or more) of the vCDR calculation result as an input value in addition to the image classification result.