arXiv Open Access 2018

A Novel Hybrid Machine Learning Model for Auto-Classification of Retinal Diseases

C. -H. Huck Yang Jia-Hong Huang Fangyu Liu Fang-Yi Chiu Mengya Gao +3 lainnya

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Abstrak

Automatic clinical diagnosis of retinal diseases has emerged as a promising approach to facilitate discovery in areas with limited access to specialists. We propose a novel visual-assisted diagnosis hybrid model based on the support vector machine (SVM) and deep neural networks (DNNs). The model incorporates complementary strengths of DNNs and SVM. Furthermore, we present a new clinical retina label collection for ophthalmology incorporating 32 retina diseases classes. Using EyeNet, our model achieves 89.73% diagnosis accuracy and the model performance is comparable to the professional ophthalmologists.

Topik & Kata Kunci

cs.CV cs.IR

Penulis (8)

C. -H. Huck Yang

Jia-Hong Huang

Fangyu Liu

Fang-Yi Chiu

Mengya Gao

Weifeng Lyu

I-Hung Lin M. D.

Jesper Tegner

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APA MLA BibTeX

Yang, C.-.H., Huang, J., Liu, F., Chiu, F., Gao, M., Lyu, W. et al. (2018). A Novel Hybrid Machine Learning Model for Auto-Classification of Retinal Diseases. https://arxiv.org/abs/1806.06423

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Informasi Jurnal

Tahun Terbit: 2018
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓