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Proceedings Paper

Automated eye disease classification method from anterior eye image using anatomical structure focused image classification technique
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Paper Abstract

This paper presents an automated classification method of infective and non-infective diseases from anterior eye images. Treatments for cases of infective and non-infective diseases are different. Distinguishing them from anterior eye images is important to decide a treatment plan. Ophthalmologists distinguish them empirically. Quantitative classification of them based on computer assistance is necessary. We propose an automated classification method of anterior eye images into cases of infective or non-infective disease. Anterior eye images have large variations of the eye position and brightness of illumination. This makes the classification difficult. If we focus on the cornea, positions of opacified areas in the corneas are different between cases of the infective and non-infective diseases. Therefore, we solve the anterior eye image classification task by using an object detection approach targeting the cornea. This approach can be said as “anatomical structure focused image classification”. We use the YOLOv3 object detection method to detect corneas of infective disease and corneas of non-infective disease. The detection result is used to define a classification result of an image. In our experiments using anterior eye images, 88.3% of images were correctly classified by the proposed method.

Paper Details

Date Published: 16 March 2020
PDF: 6 pages
Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 1131446 (16 March 2020); doi: 10.1117/12.2549951
Show Author Affiliations
Masahiro Oda, Nagoya Univ. (Japan)
Takefumi Yamaguchi, Ichikawa General Hospital (Japan)
Hideki Fukuoka, Kyoto Prefectural Univ. of Medicine (Japan)
Yuta Ueno, Univ. of Tsukuba (Japan)
Kensaku Mori, Nagoya Univ. (Japan)
National Institute of Informatics (Japan)

Published in SPIE Proceedings Vol. 11314:
Medical Imaging 2020: Computer-Aided Diagnosis
Horst K. Hahn; Maciej A. Mazurowski, Editor(s)

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