Our Projects


Glaucoma Detection

Glaucoma affects millions of people worldwide and is an eye disease that can lead to vision loss if left untreated. Open- angle glaucoma is the most common type and gradually leads to vision deterioration without many early warning signs or painful symptoms. Clinical diagnosis of glaucoma by specialists is possible but the methods used are either expensive or takes a lot of time. This project aims at automatic detection of early and advanced glaucoma using fundus images. ResNet-50 and GoogLeNet deep convolutional neural network algorithms are trained and fine-tuned using transfer learning for classification. GoogLeNet and ResNet-50 are implemented for the detection of early as well as advanced glaucoma detection.


Automated Age-Related Macular Degeneration and Diabetic Macular Edema Detection

Age-related macular degeneration (AMD) is an eye disease that damages the retina, causing vision loss. Diabetic macular edema (DME) is also a form of vision loss for diabetic people. It is therefore crucial to detect AMD and DME in the early stages for the timely treatment of the eye and the prevention of any vision impairment. Automatic detection of DME and AMD on optical coherence tomography (OCT) images are aimed in this project. The method used is based on training a deep learning algorithm to classify them into healthy, dry AMD, wet AMD and DME categories. This method is compared with a transfer learning based method proposed recently in the literature for classification of OCT images into AMD and DME categories.