A Study of the Interpretability of Fundus Analysis with Deep Learning-Based Approaches for Glaucoma Assessment

Abstract

Earlier studies focused on training ResNet50 deep learning models on a dataset of fundus images from the National Taiwan University Hospital HsinChu Branch. The study aimed to identify class-specific discriminative areas related to various conditions of ganglion cell complex (GCC) thickness, center focus areas, cropped patches from the fundus, and dataset partitions. The study utilized two visualization methods to evaluate and explain the areas of interest of the network model and determine if they aligned with clinical diagnostic knowledge. The results of the experiments demonstrated that incorporating GCC thickness information improved the accuracy of glaucoma determination. The deep learning models primarily focused on the optic nerve head (ONH) for glaucoma diagnosis, which was consistent with clinical rules. Nonetheless, the models achieved high prediction accuracy in detecting glaucomatous cases using only cropped images of macular areas. Moreover, the model’s focus on regions with GCC impairment in some cases indicates that deep learning models can identify morphologically detailed alterations in fundus photographs that may be beyond the scope of visual diagnosis by experts. This highlights the significant contribution of deep learning models in the diagnosis of glaucoma.