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.

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.

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