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

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 Classifi...

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Main Authors: Fangyuan Liu, Bo Qin, Fengqi Jiang
Format: Article
Language:English
Published: PeerJ Inc. 2023-12-01
Series:PeerJ Computer Science
Subjects:
Online Access:https://peerj.com/articles/cs-1672.pdf
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author Fangyuan Liu
Bo Qin
Fengqi Jiang
author_facet Fangyuan Liu
Bo Qin
Fengqi Jiang
author_sort Fangyuan Liu
collection DOAJ
description 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.
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spelling doaj.art-b9422cdadb6b4c859e3f929825db5b872023-12-14T15:05:07ZengPeerJ Inc.PeerJ Computer Science2376-59922023-12-019e167210.7717/peerj-cs.1672Eye Disease Net: an algorithmic model for rapid diagnosis of diseasesFangyuan Liu0Bo Qin1Fengqi Jiang2The Second Clinical Medical College, Jinan University, Shenzhen, ChinaThe Second Clinical Medical College, Jinan University, Shenzhen, ChinaThe Second Clinical Medical College, Jinan University, Shenzhen, ChinaWith 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.https://peerj.com/articles/cs-1672.pdfOphthalmic diseasesTransformer algorithmClassification effectModel structure
spellingShingle Fangyuan Liu
Bo Qin
Fengqi Jiang
Eye Disease Net: an algorithmic model for rapid diagnosis of diseases
PeerJ Computer Science
Ophthalmic diseases
Transformer algorithm
Classification effect
Model structure
title Eye Disease Net: an algorithmic model for rapid diagnosis of diseases
title_full Eye Disease Net: an algorithmic model for rapid diagnosis of diseases
title_fullStr Eye Disease Net: an algorithmic model for rapid diagnosis of diseases
title_full_unstemmed Eye Disease Net: an algorithmic model for rapid diagnosis of diseases
title_short Eye Disease Net: an algorithmic model for rapid diagnosis of diseases
title_sort eye disease net an algorithmic model for rapid diagnosis of diseases
topic Ophthalmic diseases
Transformer algorithm
Classification effect
Model structure
url https://peerj.com/articles/cs-1672.pdf
work_keys_str_mv AT fangyuanliu eyediseasenetanalgorithmicmodelforrapiddiagnosisofdiseases
AT boqin eyediseasenetanalgorithmicmodelforrapiddiagnosisofdiseases
AT fengqijiang eyediseasenetanalgorithmicmodelforrapiddiagnosisofdiseases