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High-Performance Detection of Corneal Ulceration Using Image Classification with Convolutional Neural Networks

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Title:High-Performance Detection of Corneal Ulceration Using Image Classification with Convolutional Neural Networks
Authors:Gross, Jan
Breitenbach, Johannes
Baumgartl, Hermann
Buettner, Ricardo
Keywords:Big Data on Healthcare Application
convolutional neural network-based image classification
corneal ulcer stages
it-based healthcare
Date Issued:05 Jan 2021
Abstract:Corneal Ulcer, also known as keratitis, represents the most frequently appearing symptom among corneal diseases, the second leading cause of ocular morbidity worldwide. Consequences such as irreversible eyesight damage or blindness require an innovative approach that enables a distinction to be made between patterns of different ulcer stages to lower the global burden of visual disability. This paper describes a Convolutional Neural Network-based image classification approach that allows the identification of different types of Corneal Ulcers based on fluorescein staining images. With a balanced accuracy of 92.73 percent, our results set a benchmark in distinguishing between general ulcer patterns. Our proposed method is robust against light reflections and allows automated extraction of meaningful features, manifesting a strong practical and theoretical relevance. By identifying Corneal Ulcers at an early stage, we aid reduction of aggravation by preventively applying and consequently tracking the efficacy of adapted medical treatment, which contributes to IT-based healthcare.
Pages/Duration:10 pages
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
Appears in Collections: Big Data on Healthcare Application

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