Detection of Papilledema Severity from Color Fundus Images using Transfer Learning Approaches

Abstract

Papilledema is edema in the area where the optic nerve meets the eye as a result of increased pressure inside the head. This disease can result in very serious problems, such as abnormal optical changes, decreased visual acuity, and even permanent blindness if left untreated. In this study, an image processing based solution was presented for the detection of papilledema severity from color fundus images using transfer learning approaches. The image dataset includes 295 papilledema images, 295 pseudopapilledema images, and 779 control images. Histogram equalization and the 3D box filter were used for image preprocessing. The images were enhanced with the histogram equalization method and denoised with the 3D box filter method. Then, the performances of EfficentNet-B0, GoogLeNet, MobileNetV2, NASNetMobile, and ResNet-101 transfer learning approaches were compared. The hold-out method was used to calculate the performance of transfer learning. In the experiments, the MobileNetV2 approach had the highest performance with 0.96 overall accuracy and 0.94 Cohen's Kappa. The results of the experiments proved that the combination of the histogram equalization, the 3D box filter, and the MobileNetV2 transfer learning approach can be used for automatic detection of papilledema severity. Compared to other similar studies that are known in the literature, the overall accuracy was higher.