Classifying infective keratitis using a deep learning approach

Shelda Sajeev, Mallika Prem Senthil

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publicationProceedings of the Australasian Computer Science Week Multiconference 2021, ACSW 2021
EditorsNigel Stanger, Veronica Liesaputra Joachim
PublisherAssociation for Computing Machinery (ACM)
ISBN (Electronic)9781450389563
DOIs
Publication statusPublished - 1 Feb 2021
Event2021 Australasian Computer Science Week Multiconference, ACSW 2021 - Virtual, Online, New Zealand
Duration: 1 Feb 20215 Feb 2021

Publication series

NameACM International Conference Proceeding Series

Conference

Conference2021 Australasian Computer Science Week Multiconference, ACSW 2021
CountryNew Zealand
CityVirtual, Online
Period1/02/215/02/21

Keywords

  • classification
  • convolutional neutral network
  • deep learning
  • keratitis

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