Abstract
Optical Coherence Tomography (OCT) of the human eye are used by optometrists to analyze and detect various age-related eye abnormalities like Choroidal Neovascularization, Drusen (CNV), Diabetic Macular Odeama (DME), Drusen. Detecting these diseases are quite challenging and requires hours of analysis by experts, as their symptoms are somewhat similar. We have used transfer learning with VGG16 and Inception V3 models which are state of the art CNN models. Our solution enables us to predict the disease by analyzing the image through a convolutional neural network (CNN) trained using transfer learning. Proposed approach achieves a commendable accuracy of 94% on the testing data and 99.94% on training dataset with just 4000 units of data, whereas to the best of our knowledge other researchers have achieved similar accuracies using a substantially larger (almost 10 times) dataset.
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Bhowmik, A., Kumar, S., Bhat, N. (2019). Eye Disease Prediction from Optical Coherence Tomography Images with Transfer Learning. In: Macintyre, J., Iliadis, L., Maglogiannis, I., Jayne, C. (eds) Engineering Applications of Neural Networks. EANN 2019. Communications in Computer and Information Science, vol 1000. Springer, Cham. https://doi.org/10.1007/978-3-030-20257-6_9
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