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Classifying infective keratitis using a deep learning approach

Published: 01 February 2021 Publication History

Abstract

Early diagnosis of infective keratitis is critical as it is a vision-threatening condition that can lead to significant vision loss and ocular morbidity. Diagnosis of infective keratitis done through clinical findings and slit- lamp examination is intricate and requires high expertise. Most infective keratitis cases are challenging to the clinicians. This paper proposes a deep learning approach enabling a more accurate diagnoses and treatment of infective keratitis. As a first step towards developing a comprehensive deep learning-based disease detection tool, we have classified bacterial and viral keratitis based on slit-lamp images and convolutional neutral network. A total of 446 keratitis images (bacterial – 271 and viral - 175) were available for the study. The experiment was conducted with different CNN configurations: with different input shape (image sizes: 64x64, 128x128, 256x256, 400x400) with two and three convolution layers. Image size 64x64 with three convolutional layer and no pooling achieved the highest performance (sensitivity =0.715, specificity= 0.880, precision= 0.807, accuracy= 0.812 and AUC=0.856). Experimental results show that even with a small dataset CNN was able to produce a good classification result.

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Cited By

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  • (2023)Artificial Intelligence and Infectious Keratitis: Where Are We Now?Life10.3390/life1311211713:11(2117)Online publication date: 26-Oct-2023
  • (2023)Accuracy of artificial intelligence model for infectious keratitis classification: a systematic review and meta-analysisFrontiers in Public Health10.3389/fpubh.2023.123923111Online publication date: 24-Nov-2023

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        cover image ACM Other conferences
        ACSW '21: Proceedings of the 2021 Australasian Computer Science Week Multiconference
        February 2021
        211 pages
        ISBN:9781450389563
        DOI:10.1145/3437378
        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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        Association for Computing Machinery

        New York, NY, United States

        Publication History

        Published: 01 February 2021

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        Author Tags

        1. classification
        2. convolutional neutral network
        3. deep learning
        4. keratitis

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        • Refereed limited

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        • Flinders University 2018 ECR Establishment Grant

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        ACSW '21

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        Overall Acceptance Rate 61 of 141 submissions, 43%

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        View all
        • (2023)Artificial Intelligence and Infectious Keratitis: Where Are We Now?Life10.3390/life1311211713:11(2117)Online publication date: 26-Oct-2023
        • (2023)Accuracy of artificial intelligence model for infectious keratitis classification: a systematic review and meta-analysisFrontiers in Public Health10.3389/fpubh.2023.123923111Online publication date: 24-Nov-2023

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