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This paper presents an automated classification method of infective andnon-infective diseases from anterior eye images. Treatments for cases ofinfective and non-infective diseases are different. Distinguishing them fromanterior eye images is important to decide a treatment plan. Ophthalmologistsdistinguish them empirically. Quantitative classification of them based oncomputer assistance is necessary. We propose an automated classification methodof anterior eye images into cases of infective or non-infective disease.Anterior eye images have large variations of the eye position and brightness ofillumination. This makes the classification difficult. If we focus on thecornea, positions of opacified areas in the corneas are different between casesof the infective and non-infective diseases. Therefore, we solve the anterioreye image classification task by using an object detection approach targetingthe cornea. This approach can be said as "anatomical structure focused imageclassification". We use the YOLOv3 object detection method to detect corneas ofinfective disease and corneas of non-infective disease. The detection result isused to define a classification result of a image. In our experiments usinganterior eye images, 88.3% of images were correctly classified by the proposedmethod.

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