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NeoNet: A Novel Deep Learning Model for Retinal Disease Diagnosis and Localization.

Valeria Sorgente¹, Simona Correra¹, Ilenia Verrillo¹

¹Department of Medicine and Health Sciences "Vincenzo Tiberio", University of Molise, 86100 Campobasso, Italy.

Sensors (Basel, Switzerland)

|October 16, 2025

Summary

This summary is machine-generated.

This study introduces NeoNet, an explainable deep learning model for early detection of Age-Related Macular Degeneration and Diabetic Retinopathy, achieving 99.5% accuracy. The model identifies critical image regions, aiding in precise diagnosis of retinal diseases.

Keywords:

artificial intelligence convolutional neural network deep learning localization retinal disease

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Area of Science:

Ophthalmology
Medical Imaging
Artificial Intelligence

Background:

Retinal diseases are a major cause of global vision impairment.
Early detection is crucial for effective treatment and preventing vision loss.

Purpose of the Study:

To develop an explainable deep learning method for identifying and localizing retinal conditions.
To specifically target Age-Related Macular Degeneration, Diabetic Retinopathy, and Choroidal Neovascularization.

Main Methods:

Utilized seven fine-tuned convolutional neural networks (MobileNet, LeNet, StandardCNN, CustomCNN, DenseNet, Inception, EfficientNet).
Developed a novel architecture, NeoNet, specifically for retinal disease detection.
Implemented explainable AI techniques to highlight critical image regions for model predictions.

Main Results:

NeoNet achieved a diagnostic accuracy of 99.5%.
The model successfully identified pathological features in retinal images.
Explainability methods pinpointed image regions influencing diagnostic decisions.

Conclusions:

The proposed NeoNet model demonstrates high accuracy in detecting multiple retinal diseases.
Explainable AI enhances diagnostic support by revealing decision-making processes.
This approach facilitates earlier and more accurate diagnosis of vision-threatening retinal conditions.