Deep Transfer Learning for Improved Detection of Keratoconus using Corneal Topographic Maps
Abstract
Clinical keratoconus (KCN) detection is a challenging and time-consuming task. In the diagnosis process, ophthalmologists must revise demographic and clinical ophthalmic examinations. The latter include slit-lamb, corneal topographic maps, and Pentacam indices (PI). We propose an Ensemble of Deep Transfer Learning (EDTL) based on corneal topographic maps. We consider four pretrained networks, SqueezeNet (SqN), AlexNet (AN), ShuffleNet (SfN), and...
Paper Details
Title
Deep Transfer Learning for Improved Detection of Keratoconus using Corneal Topographic Maps
Published Date
Jun 16, 2021
Journal
Volume
14
Issue
5
Pages
1627 - 1642
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