Novel Metaheuristic Algorithms and Their Applications to Efficient Detection of Diabetic Retinopathy DOI Open Access
M. Hassaballah, Mohamed Abdel Hameed

Journal of Artificial Intelligence and Soft Computing Research, Journal Year: 2024, Volume and Issue: 15(2), P. 167 - 195

Published: Dec. 1, 2024

Abstract It is an extremely important to have AI-based system that can assist specialties correctly identify and diagnosis diabetic retinopathy (DR). In this study, we introduce accurate approach for DR using machine learning (ML) techniques a modified golf optimization algorithm (mGOA). The mGOA optimizes ML classifiers through finding the best available parameters with respect objective functions, hence decreases number of features increases classifier’s accuracy. A fitness function employed minimize feature medical dataset. obtained results showed superiority higher convergence speeds without extra processing costs across datasets compared several competitors. Also, attained maximum accuracy optimally reduced in binary multi-class achieving CEC’2022 benchmark other metaheuristic algorithms. Based on findings, three optimized called mGOA-SVM, mGOA-radial SVM,and mGOA-kNN were introduced as tools classification disease their performance was assessed Messidor EyePACS1 datasets. Experimental demonstrated mGOA-SVM SVM achieved remarkable average 98.5% precision 97.4%.

Language: Английский

Advances in Deep Learning for Medical Image Analysis: A Comprehensive Investigation DOI
Rajeev Ranjan Kumar, S. Vishnu Shankar, Ronit Jaiswal

et al.

Journal of Statistical Theory and Practice, Journal Year: 2025, Volume and Issue: 19(1)

Published: Jan. 23, 2025

Language: Английский

Citations

2
Classification method for nailfold capillary images using an optimized sugeno fuzzy ensemble of convolutional neural networks DOI

Chiao-Chi Ou,

Yijin Liu, Kuo-Ping Lin

et al.

Computers in Biology and Medicine, Journal Year: 2025, Volume and Issue: 189, P. 109975 - 109975

Published: March 6, 2025

Language: Английский

Citations

1
Multiple teachers are beneficial: A lightweight and noise-resistant student model for point-of-care imaging classification DOI
Yucheng Song, Anqi Song, J.T.L. Wang

et al.

Expert Systems with Applications, Journal Year: 2025, Volume and Issue: unknown, P. 127145 - 127145

Published: March 1, 2025

Language: Английский

Citations

0
Automated diabetic retinopathy detection using a multi-step framework with stacked ensemble-based classification model DOI

Santoshkumar S Ainapur,

Virupakshappa Patil

Expert Systems with Applications, Journal Year: 2025, Volume and Issue: unknown, P. 127709 - 127709

Published: April 1, 2025

Language: Английский

Citations

0
Optimizing diabetic retinopathy detection with electric fish algorithm and bilinear convolutional networks DOI Creative Commons

Udayaraju Pamula,

Venkateswararao Pulipati,

Gamini Suresh

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: April 23, 2025

Diabetic Retinopathy (DR) is a leading cause of vision impairment globally, necessitating regular screenings to prevent its progression severe stages. Manual diagnosis labor-intensive and prone inaccuracies, highlighting the need for automated, accurate detection methods. This study proposes novel approach early DR by integrating advanced machine learning techniques. The proposed system employs three-phase methodology: initial image preprocessing, blood vessel segmentation using Hopfield Neural Network (HNN), feature extraction through an Attention Mechanism-based Capsule (AM-CapsuleNet). features are optimized Taylor-based African Vulture Optimization Algorithm (AVOA) classified Bilinear Convolutional (BCAN). To enhance classification accuracy, introduces hybrid Electric Fish Arithmetic (EFAOA), which refines exploration phase, ensuring rapid convergence. model was evaluated on balanced dataset from APTOS 2019 Blindness Detection challenge, demonstrating superior performance in terms accuracy efficiency. offers robust solution DR, potentially improving patient outcomes timely precise diagnosis.

Language: Английский

Citations

0
Advanced Detection of Diabetic Retinopathy: Employing Hybrid Deep Neural Networks and Multi-Scale Image Analysis Techniques DOI Open Access

A. M. Mutawa,

G. R. Hemalakshmi,

N. B. Prakash

et al.

Published: Jan. 30, 2024

Diabetic retinopathy (DR), a major complication of prolonged diabetes, poses significant risk vision loss. Early detection is critical for effective treatment, yet traditional diagnostic methods by ophthalmologists are time-consuming, costly, and subject to variability. This study introduces novel approach employing hybrid Convolutional Neural Network-Radial Basis Function (CNN-RBF) classifier integrated with Multi-Scale Discriminative Robust Local Binary Pattern (MS-DRLBP) features enhanced DR detection. We implemented advanced image preprocessing techniques, including noise reduction, morphological operations, Otsu’s thresholding, optimize blood vessel segmentation from retinal images. Our method demonstrates exceptional performance in screening DR, achieving an average 96.10% precision, 95.35% sensitivity, 97.06% specificity, accuracy. These results significantly outperform offer promising tool remote efficient DR. Applied publicly available datasets, this research contributes the development accessible, accurate ophthalmology, potentially reducing global burden diabetic

Language: Английский

Citations

1
Machine Learning and Deep Learning Approaches for Automated Diabetic Retinopathy Diagnosis DOI

Bhushan Fulkar,

Rushikesh Burle,

Pawan Patil

et al.

Published: May 3, 2024

Language: Английский

Citations

1
Application of Artificial Intelligence for Classification, Segmentation, Early Detection, Early Diagnosis, and Grading of Diabetic Retinopathy from Fundus Retinal Images: A Comprehensive Review DOI Creative Commons

G.Kalaimathi Priya S.Rajarajeshwari,

G. Chemmalar Selvi

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 172499 - 172536

Published: Jan. 1, 2024

Language: Английский

Citations

1
Advancements in Deep Learning for Automated Diagnosis of Ophthalmic Diseases: A Comprehensive Review DOI Creative Commons

Soubhagya Kumar Dash,

Prabira Kumar Sethy, Ashis Das

et al.

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 171221 - 171240

Published: Jan. 1, 2024

Language: Английский

Citations

1
Classification of Diabetic Retinopathy Using Deep Convolutional Neural Networks (DCNN) DOI Creative Commons

Mahalakshmi Bollimuntha,

p - Hyndavi,

K. Deepthi

et al.

International Journal For Multidisciplinary Research, Journal Year: 2024, Volume and Issue: 6(2)

Published: April 5, 2024

Diabetic Retinopathy (DR) is a leading cause of vision impairment and blindness among individuals with diabetes. Early detection accurate classification DR stages are crucial for timely intervention effective management. In recent years, Deep learning (DL) methods have emerged as powerful tools image analysis, demonstrating remarkable success in various medical imaging applications. Large dataset, processing difficulty, complex training computation time the major drawbacks existing work by using support vector machine (SVM) method. The objective this proposed system gives proper results Convolutional neural networks (DCNNs) techniques high accuracy feature analysis blood vessels.

Language: Английский

Citations

0