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Automated Computer Aided diagnostic tools can be used for the early detectionof glaucoma to prevent irreversible vision loss. In this work, we present aMulti-task Convolutional Neural Network (CNN) that jointly segments the OpticDisc (OD), Optic Cup (OC) and predicts the presence of glaucoma in color fundusimages. The CNN utilizes a combination of image appearance features andstructural features obtained from the OD-OC segmentation to obtain a robustprediction. The use of fewer network parameters and the sharing of the CNNfeatures for multiple related tasks ensures the good generalizability of thearchitecture, allowing it to be trained on small training sets. Thecross-testing performance of the proposed method on an independent validationset acquired using a different camera and image resolution was found to be goodwith an average dice score of 0.92 for OD, 0.84 for OC and AUC of 0.95 on thetask of glaucoma classification illustrating its potential as a mass screeningtool for the early detection of glaucoma.

A Deep Learning based Joint Segmentation and Classification Framework for Glaucoma Assesment in Retinal Color Fundus Images