Deep learning-based algorithm for automated detection of glaucoma on eye fundus images

Projections predict that about one hundred and twelve million people will be affected by glaucoma by 2040. It can be ranked as a serious public health problem, being a significant cause of blindness. However, if detected early, total blindness can be delayed. A computerized analysis of images of the eye fundus can be a tool for early diagnosis of glaucoma. In this paper, we have developed a deep-learning-based algorithm for the automated detection of this condition using images from Origa-light and Origa databases. A total of 1300 images were used in the study. The algorithm consists of two steps, namely processing and classification. The images were processed respectively by blue component extraction, conversion into greyscale images, ellipse fitting, median filtering, sobel filter application and finally binarizing by a simple global thresholding method. The classification was carried out using a modified VGGNet19 (Visual Geometric Group Net 19) powered by transfer learning. The algorithm was tested on 260 images. A sensitivity of 100%, a specificity of 97.69%, an accuracy of 98.84%, an F1 score of 98.85%, and finally an area under the ROC-curve (AUC) of 0.989 were obtained. These values are encouraging and better than those yielded by many state-of-the-art methods.