Pseudopapilledema diagnosis based on a hybrid approach using deep transfer learning

Abstract

This Papilledema is edema caused by elevated pressure inside the brain near the area that leads the optic nerve to reach the eye. If left untreated, this condition can cause severe difficulties, for instance, aberrant optical changes, reduced sharpness of vision, and irreversible blindness. At present, an approach based on image processing for determining the degree of papilledema from color fundus images was given utilizing transfer learning approaches. The used dataset here contains 295 papilledema images, 295 pseudopapilledema images, and 779 control images. For the image preparation, a segmentation optimizer was utilized. The performance of the transfer learning techniques GoogleNet, MobileNetV2, ResNet-18, and ResNet-50 was then compared. Furthermore, Sensitivity and specificity and constructed ROC curves were calculated. The ResNet-50 employing the optimizer ADAM method performed best in the testing, with 98% total accuracy. The findings of the studies demonstrated that a combination of segmentation, optimization models, and transfer learning techniques may be utilized to determine the severity of papilledema automatically. The total accuracy was higher when compared to other similar studies described in the literature.