Eye Disease Net: an algorithmic model for rapid diagnosis of diseases

Fangyuan Liu; Bo Qin; Fengqi Jiang

doi:10.7717/peerj-cs.1672

Eye Disease Net: an algorithmic model for rapid diagnosis of diseases

PeerJ Comput Sci. 2023 Dec 12:9:e1672. doi: 10.7717/peerj-cs.1672. eCollection 2023.

Authors

Fangyuan Liu¹, Bo Qin^{1

2

3}, Fengqi Jiang¹

Affiliations

¹ The Second Clinical Medical College, Jinan University, Shenzhen, China.
² Shenzhen Aier Eye Hospital, Aier Eye Hospital, Jinan University, Shenzhen, China.
³ Shenzhen Aier Ophthalmic Technology Institute, Shenzhen, China.

Abstract

With the development of science and technology and the improvement of the quality of life, ophthalmic diseases have become one of the major disorders that affect the quality of life of people. In view of this, we propose a new method of ophthalmic disease classification, ED-Net (Eye Disease Classification Net), which is composed of the ED_Resnet model and ED_Xception model, and we compare our ED-Net method with classical classification algorithms, transformer algorithm, more advanced image classification algorithms and ophthalmic disease classification algorithms. We propose the ED_Resnet module and ED_Xception module and reconstruct these two modules into a new image classification algorithm ED-Net, and compared them with classical classification algorithms, transformer algorithms, more advanced image classification algorithms and eye disease classification algorithms.

Keywords: Classification effect; Model structure; Ophthalmic diseases; Transformer algorithm.

Grants and funding

The study was funded by the Science and Technology Innovation Program of Hunan Province (Grant No. 2021JJ30045). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.