  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 a image. In our experiments using
anterior eye images, 88.3% of images were correctly classified by the proposed
method.
