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International Ophthalmology
Erschienen in: International Ophthalmology 10/2023

08.06.2023 | Original Paper

EyeCNN: exploring the potential of convolutional neural networks for identification of multiple eye diseases through retinal imagery

verfasst von: Abdul Rafay, Zaeem Asghar, Hamza Manzoor, Waqar Hussain

Erschienen in: International Ophthalmology | Ausgabe 10/2023

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Abstract

Background

The eyes are the most important part of the human body as these are directly connected to the brain and help us perceive the imagery in daily life whereas, eye diseases are mostly ignored and underestimated until it is too late. Diagnosing eye disorders through manual diagnosis by the physician can be very costly and time taking.

Objective

Thus, to tackle this, a novel method namely EyeCNN is proposed for identifying eye diseases through retinal images using EfficientNet B3.

Methods

A dataset of retinal imagery of three diseases, i.e. Diabetic Retinopathy, Glaucoma, and Cataract is used to train 12 convolutional networks while EfficientNet B3 was the topperforming model out of all 12 models with a testing accuracy of 94.30%.

Results

After preprocessing of the dataset and training of models, various experimentations were performed to see where our model stands. The evaluation was performed using some well-defined measures and the final model was deployed on the Streamlit server as a prototype for public usage. The proposed model has the potential to help diagnose eye diseases early, which can facilitate timely treatment.

Conclusion

The use of EyeCNN for classifying eye diseases has the potential to aid ophthalmologists in diagnosing conditions accurately and efficiently. This research may also lead to a deeper understanding of these diseases and it may lead to new treatments. The webserver of EyeCNN can be accessed at (https://​abdulrafay97-eyecnn-app-rd9wgz.​streamlit.​app/​).
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Metadaten
Titel
EyeCNN: exploring the potential of convolutional neural networks for identification of multiple eye diseases through retinal imagery
verfasst von
Abdul Rafay
Zaeem Asghar
Hamza Manzoor
Waqar Hussain
Publikationsdatum
08.06.2023
Verlag
Springer Netherlands
Erschienen in
International Ophthalmology / Ausgabe 10/2023
Print ISSN: 0165-5701
Elektronische ISSN: 1573-2630
DOI
https://doi.org/10.1007/s10792-023-02764-5

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