title: Transfer learning based classification of optical coherence tomography images with diabetic macular edema and dry age-related macular degeneration.
abstract: We present an algorithm for identifying retinal pathologies given retinal optical coherence tomography (OCT) images. Our approach fine-tunes a pre-trained convolutional neural network (CNN), GoogLeNet, to improve its prediction capability (compared to random initialization training) and identifies salient responses during prediction to understand learned filter characteristics. We considered a data set containing subjects with diabetic macular edema, or dry age-related macular degeneration, or no pathology. The fine-tuned CNN could effectively identify pathologies in comparison to classical learning. Our algorithm aims to demonstrate that models trained on non-medical images can be fine-tuned for classifying OCT images with limited training data.
authors.affiliation: School of Medical Science and Technology, IIT Kharagpur, Kharagpur, India.
authors.forename: S P K
authors.initials: SP
authors.lastname: Karri
authors.affiliation: Department of Mathematics, IIT Kharagpur, Kharagpur, India.
authors.forename: Debjani
authors.initials: D
authors.lastname: Chakraborty
authors.affiliation: School of Medical Science and Technology, IIT Kharagpur, Kharagpur, India.
authors.forename: Jyotirmoy
authors.initials: J
authors.lastname: Chatterjee
keywords: (070.5010) Pattern recognition; (100.2960) Image analysis; (110.4500) Optical coherence tomography; (170.1610) Clinical applications; (170.4470) Ophthalmology