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Deep Neural Networks for Image-Based Dietary Assessment

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Fundus Image-Based Eye Disease Detection Using EfficientNetB3 Architecture.

Rahaf Alsohemi¹, Samia Dardouri^1,2

¹Department of Computer Science, College of Computing and Information Technology, Shaqra University, Shaqra 11911, Saudi Arabia.

Journal of Imaging

|August 27, 2025

Summary

This summary is machine-generated.

A deep learning model accurately classifies retinal diseases like diabetic retinopathy, cataract, and glaucoma from fundus images, achieving 95.12% accuracy. This automated approach aids early diagnosis and prevents vision loss.

Keywords:

CNN EfficientNetB0 cataract deep learning diabetic retinopathy eye disease classification fundus images glaucoma image augmentation medical image analysis

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Area of Science:

Ophthalmology
Computer Science
Artificial Intelligence

Background:

Early detection of retinal diseases is crucial for preventing vision loss.
Manual diagnosis of fundus images is time-consuming and prone to errors.
Automated solutions are needed to improve diagnostic efficiency and accuracy.

Purpose of the Study:

To develop and evaluate a deep learning model for automated classification of retinal diseases.
To categorize fundus images into four classes: cataract, diabetic retinopathy, glaucoma, and healthy.
To assess the model's performance using various classification metrics.

Main Methods:

Utilized a pretrained EfficientNetB3 architecture for image classification.
Fine-tuned the model on a public Kaggle retinal image dataset.
Employed transfer learning, data augmentation, and the Adam optimizer with a cosine annealing scheduler.

Main Results:

Achieved a high classification accuracy of 95.12%.
Demonstrated strong performance with precision (95.21%), recall (94.88%), F1-score (95.00%), Dice Score (94.91%), Jaccard Index (91.2%), and MCC (0.925).
The model showed robustness in classifying four distinct retinal conditions.

Conclusions:

The proposed deep learning model shows significant potential for automated retinal disease diagnosis.
This automated system can support clinical decision-making and improve patient outcomes.
Further validation in clinical settings is warranted to confirm its utility.