# Identification of Retinal Diseases Using Light Convolutional Neural Networks and Intrinsic Mode Function Technique

## TL;DR

This paper introduces a new method combining signal processing and a lightweight neural network to accurately detect retinal diseases from eye images.

## Contribution

The novel hybrid model integrates IMF filtering with LightCNN for improved fundus image classification.

## Key findings

- The proposed model achieves 99.4% accuracy on retinal disease classification.
- It outperforms conventional CNN and ResNet models in precision, recall, and F1-score.
- The hybrid approach enhances diagnostic accuracy and computational efficiency.

## Abstract

Background/Objectives: Fundus imaging provides a detailed view of the interior surface of the eye and plays a crucial role in the early diagnosis of retinal diseases. However, automated interpretation of fundus images remains challenging due to variations in illumination, noise, and structural complexity. Methods: A novel hybrid model that integrates the Intrinsic Mode Function (IMF) filter, derived from Empirical Mode Decomposition (EMD), with a Light Convolutional Neural Network (LightCNN) for enhanced fundus image classification was proposed. The IMF filter effectively decomposes the input signal into intrinsic components, isolating high-frequency noise and preserving critical retinal patterns. These refined components are subsequently processed by the LightCNN architecture, which offers lightweight yet highly discriminative feature extraction and classification capabilities. Results: Experimental results on DIARETDB fundus datasets demonstrate that the proposed IMF + LightCNN model achieves 99.4% accuracy, 99.1% precision, 98.87% recall, and a 98.31 F1-score, significantly outperforming conventional CNN and ResNet-based models. Conclusions: Integrating advanced signal processing with lightweight deep learning improves both diagnostic accuracy and computational efficiency. This hybrid framework establishes a promising pathway for reliable and real-time clinical screening of retinal diseases.