Gaze Fusion-Deep Neural Network Model for Glaucoma Detection

The proposed system, Gaze Fusion - Deep Neural Network Model (GFDM) has utilized transfer learning approach to discriminate subject’s eye tracking data in the form of fusion map into two classes: glaucoma and normal. We have fed eye tracking data in the form of fusion maps of different participants to Deep Neural Network (DNN) model which is pretrained with ImageNet weights. The experimental results of the GFDM show that fusion map dissimilar to pretrained model’s dataset can give better understanding of glaucoma. The model also show the part of the screen where participants has the difficulty in viewing. GFDM has compared with traditional machine learning models such as Support Vector Classifier, Decision Tree classifier and ensemble classifier and shown that the proposed model outperforms other classifiers. The model has Area Under ROC Curve (AUC) score 0.75. The average sensitivity of correctly identifying glaucoma patients is 100% with specificity value 83%.