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Updated: Jul 2, 2025

Quantification of Vascular Parameters in Whole Mount Retinas of Mice with Non-Proliferative and Proliferative Retinopathies

Quantification of Vascular Parameters in Whole Mount Retinas of Mice with Non-Proliferative and Proliferative Retinopathies

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An enumerative pre-processing approach for retinopathy severity grading using an interpretable classifier: a

Hemanth Kumar Vasireddi^1,2, Suganya Devi K³, G N V Raja Reddy^1,4

¹Computer Science and Engineering, National Institute of Technology, Silchar, 788010, Assam, India.

Graefe'S Archive for Clinical and Experimental Ophthalmology = Albrecht Von Graefes Archiv Fur Klinische Und Experimentelle Ophthalmologie

|February 24, 2024

Summary

An artificial intelligence (AI) system was developed to improve diabetic retinopathy (DR) screening. The novel approach achieved high accuracy in DR severity grading, enhancing diagnostic efficacy.

Keywords:

Artificial intelligence Deep learning Diabetic retinopathy Interpretable classifier Neural networks Optimization Pre-processing

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Last Updated: Jul 2, 2025

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Published on: March 12, 2022

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Area of Science:

Ophthalmology
Medical Imaging
Artificial Intelligence

Background:

Diabetic retinopathy (DR) is a leading cause of irreversible vision loss.
Increasing DR prevalence necessitates efficient diagnostic tools.
Current artificial intelligence (AI) systems for DR screening require accuracy improvements.

Purpose of the Study:

To develop and implement an AI-based screening system for diabetic retinopathy (DR) using color fundus photographs.
To enhance the accuracy and efficiency of DR diagnosis.

Main Methods:

An enumerative pre-processing approach was integrated into a deep learning model for DR severity grading.
The proposed model was compared against various pre-trained models and optimization algorithms.
Performance metrics including accuracy, precision, recall, and F1 score were evaluated on the MESSIDOR dataset.

Main Results:

The enumerative pipeline combination K1-K2-K3-DFNN-LOA demonstrated superior performance.
The proposed model achieved a maximum accuracy of 97.60%, precision of 94.60%, recall of 98.40%, and F1 score of 94.60%.
The macro-averaged metric reached 0.97, indicating robust performance.

Conclusions:

The developed AI system effectively screens diabetic retinopathy from color fundus images.
This system has the potential to improve the efficiency and accessibility of DR diagnosis.
Further advancements in AI can enhance clinical decision-making for eye conditions.