Thick Data Analytics for Identifying Eye Conditions using Siamese Lookalike Neural Networ

Human’s perceptual ruling on image similarity uses a phenomenon-learning model called pareidolia to recognize shapes like faces from in random samples. This short paper is an attempt to present a learning model that has the capability to recognize eye conditions from small training sample. The proposed model uses a LookALike learning technique to attempt providing higher perceptual capability that goes beyond the normal supervised learning. The proposed model employs a Siamese neural net with triplet loss function as well as two thick data analytics methods involving retina vessels augmentation and dropout layers. The model is trained on 400 eye conditions dataset from Kaggle where it distributed on four categories (cataract, glaucoma, Diabetic Retinopathy and Retina_Disease) as well as generative model to distribute lookalike eye conditions as closely similar to anchor, positively related to anchor or negatively related to anchor). The LookALike learning model shows 60% accuracy tested on 100 new eye cases and is improved to 70% accuracy when we added the iris vessels augmentation and other dropout filters. We are intending to use more thick data learning techniques to improve its performance among other research investigations, which we leave it to our future research.