Work

Multi‐Disease Detection in Retinal Imaging Using VNet with Image Processing Methods for Data Generation

Year: 2025

Type: article

Abstract: Deep learning faces challenges like limited data, vanishing gradients, high parameter counts, and long training times. This article addresses two key issues: 1) data scarcity in ophthalmology and 2) vanishing gradients in deep networks. To overcome data limitations, an image processing‐based data generation method is proposed, expanding the dataset size by 12x. This approach enhances model training and prevents overfitting. For vanishing gradients, a deep neural network is introduced with optimized weight updates in initial layers, enabling the use of more and deeper layers. The proposed methods are validated using the retinal fundus multi‐disease image database dataset, a limited and imbalanced ophthalmology dataset available on the Grand Challenge website. Results show a 10% improvement in model accuracy compared to the original dataset and a 5% improvement over the benchmark reported on the website.

Source: Advanced Intelligent Systems

DOI: 10.1002/aisy.202401039

Authors: Samad Azimi Abriz, Mansoor Fateh, Fatemeh Jafarinejad, Vahid Abolghasemi

Institutions: University of Shahrood, University of Essex

Language: English

Topic: Retinal Imaging and Analysis

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $3,970