Determination of the activation regions of three deep learning models in infrared images of glaucoma

Abstract

AbstractPurpose: The main objective of this study is to characterize the activation regions of three Deep Learning models using infrared images of the optic nerve of glaucoma patients.Methods: We have retrospectively collected a sample of patients with primary and secondary open‐angle glaucoma and normal patients. The images in infrared were recorded with a spectral domain optical coherence tomography. Three previously trained models were used, VGG19, ResNet101 and the Shufflenet. Sensitivity, specificity, diagnostic accuracy in training and testing, and ROC area were calculated for all three models. The gradient‐weighted class activation map (GradCAM) was used to obtain the activation regions that highlight the most important components in the images for model prediction. The correlation coefficient between the glaucoma activation maps and between normal activation maps of the three models was calculated, and the location of the activation regions in the glaucoma and normal images was determined for each model.Results: 639 eyes of 415 patients were collected, 432 glaucomatous and 207 normal. The accuracy of the models on the test set was 96.9% (VGG19), 95.3% (ResNet101), and 93.8% (Shufflenet). The activation maps of the ResNet101 and Shufflenet models obtained a high correlation in glaucoma (0.75) and normal (0.68) cases. For the three models, the region of interest was located mainly in the inferior temporal quadrant in 65.2% of the cases (VGG19), in 52.9% (Shufflenet) and in 54.1% (ResNet101).Conclusions: In glaucoma eyes, the regions of interest assessed with the gradient‐weighted class activation maps of the three models analysed are located in more than half of the cases in the inferior temporal quadrant of the optic nerve.