| Abstrakt: |
Glaucoma is a typical neurodegenerative disease that causes permanent vision deficits. Early detection is important to prevent vision loss as the patient is unaware of the damage and severity because pain is not induced during the process and progresses gradually. To predict glaucoma, fundus image-based deep learning models are being actively developed. In the traditional approach, original fundus photographs including red, green, and blue (RGB) channels were used as a learning material of deep learning models. In this paper, we propose a new pre-processing method to produce the learning images by segmenting vascular, optic nerve papillae, and optic nerve indentation images from the original fundus images, and we built a deep learning model; previous RGB channels are replaced into our three segmented images. We test the proposed method using two datasets and confirm that proposed method shows better performance than previous approaches that use original fundus photographs. [ABSTRACT FROM AUTHOR] |
