Transfer Learning and Optimized Machine Learning Techniques for Multiclass Diabetic Retinopathy Classification Using Retinal Images.

Overview

1. Department of Electrical Engineering, Na.c., Islamic Azad University, Najafabad 8514143131, Iran
2. Digital Processing and Machine Vision Research Center, Na.c., Islamic Azad University, Najafabad 8514143131, Iran
3. School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran 1439957131, Iran
4. School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran 1956836613, Iran

Abstract

Background/Objectives: Diabetic Retinopathy (DR) is a prevalent and severe complication of diabetes, caused by prolonged hyperglycemia that damages retinal microvasculature and may ultimately lead to vision loss or blindness. While convolutional neural networks (CNNs) have shown promise in automating DR detection via retinal imaging, traditional approaches often suffer from limited diagnostic accuracy, long training times, and reliance on small or imbalanced datasets. Objective: This study evaluates an integrated transfer-learning using adaptive training strategies for multiclass retinal image classification. Methods: The proposed framework integrates transfer learning, feature-space dimensionality reduction, and adaptive training strategies based on an ImageNet pretrained ResNet50 backbone to improve training stability, computational efficiency, and multiclass retinal image classification performance. Results: The proposed Transfer Learning (TL)-based model was trained and evaluated on a large, publicly available dataset of retinal images, achieving an overall accuracy of 84%, maximum class-specific accuracy of 89%, sensitivity of up to 97%, and an F1-score of 92%. These results demonstrate reasonable overall classification performance under constrained data conditions. Conclusions: The proposed framework demonstrates the feasibility of integrating transfer learning and adaptive training strategies for multiclass retinal image classification under constrained benchmark conditions. However, the study is limited by the use of heavily downsampled retinal images, and further validation on high-resolution clinical datasets is required before practical deployment. Future methodological refinement and validation on high-resolution clinical datasets may support development of computer-assisted retinal image analysis systems.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Data

Datasets cited

• kaggle.com/datasets/tanlikesmath, at Kaggle; found in “Data Availability Statement”

Data Availability Statement

The datasets analyzed during this study are available in the Kaggle repository: https://www.kaggle.com/datasets/tanlikesmath/diabetic-retinopathy-resized/data (accessed 20 May 2019).

Reproduced under the paper's license (CC BY), from the paper cited above.

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 7 keywords, 34 references.

Cite

This paper

Yousefi, M. R., Bakrani, A., Ebrahimzadeh, E., & Dehghani, A. (2026). Transfer Learning and Optimized Machine Learning Techniques for Multiclass Diabetic Retinopathy Classification Using Retinal Images. Diagnostics (Basel, Switzerland), 16(14), 2189. https://doi.org/10.3390/diagnostics16142189

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