Improving Fundus Detection Precision in Diabetic Retinopathy Using Derivative-Based Deep Neural Networks  

作　　者：Asma Aldrees Hong Min Ashit Kumar Dutta Yousef Ibrahim Daradkeh Mohd Anjum 

机构地区：[1]Department of Informatics and Computer Systems,College of Computer Science,King Khalid University,Abha,61421,Saudi Arabia [2]School of Computing,Gachon University,Seongnam,13120,Republic of Korea [3]Department of Computer Science and Information Systems,College of Applied Sciences,AlMaarefa University,Ad Diriyah,Riyadh,13713,Saudi Arabia [4]Department of Computer Engineering and Information,College of Engineering in Wadi Alddawasir,Prince Sattam bin Abdulaziz University,Al-Kharj,16273,Saudi Arabia [5]Department of Computer Engineering,Aligarh Muslim University,Aligarh,202002,India

出　　处：《Computer Modeling in Engineering & Sciences》2025年第3期2487-2511,共25页工程与科学中的计算机建模(英文)

基　　金：supported by the Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(No.2021R1F1A1055408);supported by the Researchers Supporting Project Number(MHIRSP2024005)Almaarefa University,Riyadh,Saudi Arabia.

摘　　要：Fundoscopic diagnosis involves assessing the proper functioning of the eye’s nerves,blood vessels,retinal health,and the impact of diabetes on the optic nerves.Fundus disorders are a major global health concern,affecting millions of people worldwide due to their widespread occurrence.Fundus photography generates machine-based eye images that assist in diagnosing and treating ocular diseases such as diabetic retinopathy.As a result,accurate fundus detection is essential for early diagnosis and effective treatment,helping to prevent severe complications and improve patient outcomes.To address this need,this article introduces a Derivative Model for Fundus Detection using Deep NeuralNetworks(DMFD-DNN)to enhance diagnostic precision.Thismethod selects key features for fundus detection using the least derivative,which identifies features correlating with stored fundus images.Feature filtering relies on the minimum derivative,determined by extracting both similar and varying textures.In this research,the DNN model was integrated with the derivative model.Fundus images were segmented,features were extracted,and the DNN was iteratively trained to identify fundus regions reliably.The goal was to improve the precision of fundoscopic diagnosis by training the DNN incrementally,taking into account the least possible derivative across iterations,and using outputs from previous cycles.The hidden layer of the neural network operates on the most significant derivative,which may reduce precision across iterations.These derivatives are treated as inaccurate,and the model is subsequently trained using selective features and their corresponding extractions.The proposed model outperforms previous techniques in detecting fundus regions,achieving 94.98%accuracy and 91.57%sensitivity,with a minimal error rate of 5.43%.It significantly reduces feature extraction time to 1.462 s and minimizes computational overhead,thereby improving operational efficiency and scalability.Ultimately,the proposed model enhances diagnostic precision and re

关 键 词：Deep neural network feature extraction fundus detection medical image processing 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]