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Segmentation of Fundus Photographs for Deep Learning-based Glaucoma Diagnostic Model
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딥러닝 기반 녹내장 진단 모델을 위한 안저사진 분할

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Type
Academic journal
Author
Dae-Il Jung (AIDOT) Seong Jae Kim (경상국립대학교) Kyong Jin Cho (단국대학교) Sejong Oh (단국대학교)
Journal
The Korea Institute of Information and Communication Engineering Journal of the Korea Institute of Information and Communication Engineering Vol.27 No.2 KCI Accredited Journals
Published
2023.2
Pages
186 - 191 (6page)
DOI
10.6109/jkiice.2023.27.2.186

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Segmentation of Fundus Photographs for Deep Learning-based Glaucoma Diagnostic Model
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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.
#녹내장 #이미지 분할 #딥러닝 #특징 추출 #진단 #Glaucoma #Image Segmentation #Deep Learning #Feature Extraction #Diagnosis

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ABSTRACT
Ⅰ. 서론
Ⅱ. 본론
Ⅲ. 실험 결과
Ⅳ. 결론
References

References (10)

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Y. C. Tham, X. Li, T. Y. Wong, H. A. Quigley, and T. Aung, "Global prevalence of glaucoma and projections of glaucoma burden through 2040: a systematic review and meta-analysis," Ophthalmology, vol. 121, no. 11, pp.2081-2090, Nov. 2014. DOI:10.1016/j.opht google schola V. Gulshan, L. Peng, M. Coram, M. C. Stumpe, and D. Wu, "Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs," Journal of the American Medical Association, vol. 316, no. 22, pp.2402-2 google schola P. M. Burlina, N. Joshi, M. Pekala, K. D. Pacheco, and D. E. Freund, "Automated grading of age-related macular degeneration from color fundus images using deep convolutional neural networks," Jama Ophthalmology, vol. 135, no 11, pp.1170-1176, Nov. 2017. D google schola M. D. Abràmoff, P. T. Lavin, M. Birch, N. Shah, and J. C. Folk, "Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices," NPJ digital medicine, vol. 1, No. 1, pp.1-8, Aug. 2018. DOI:10.1038/ google schola J. I. Orlando, H. Fu, J. B. Breda, K. van Keer, D. R. Bathula, and Diaz-Pinto, "Refuge challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs," Medical image analysis, vol. 59, 101570, Jan. 2020. DO google schola

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