Diabetic retinopathy detection using ensembled transfer learning based thrice CNN with SVM classifier

Multimedia Tools and Applications, Journal Year: 2024, Volume and Issue: 83(27), P. 70089 - 70115

Published: Jan. 30, 2024

Language: Английский

---

Deep Transfer Learning-Based Automated Diabetic Retinopathy Detection Using Retinal Fundus Images in Remote Areas

International Journal of Computational Intelligence Systems, Journal Year: 2024, Volume and Issue: 17(1)

Published: May 29, 2024

Abstract Diabetic retinopathy (DR) significantly burdens ophthalmic healthcare due to its wide prevalence and high diagnostic costs. Especially in remote areas with limited medical access, undetected DR cases are on the rise. Our study introduces an advanced deep transfer learning-based system for real-time detection using fundus cameras address this. This research aims develop efficient timely assistance patients, empowering them manage their health better. The proposed leverages imaging collect retinal images, which then transmitted processing unit effective disease severity classification. Comprehensive reports guide subsequent actions based identified stage. achieves by utilizing learning algorithms, specifically VGGNet. system’s performance is rigorously evaluated, comparing classification accuracy previous outcomes. experimental results demonstrate robustness of system, achieving impressive 97.6% during phase, surpassing existing approaches. Implementing automated has transformed dynamics, enabling early, cost-effective diagnosis millions. also streamlines patient prioritization, facilitating interventions early-stage cases.

Language: Английский

---

Retinal Fundus Imaging-Based Diabetic Retinopathy Classification using Transfer Learning and Fennec Fox Optimization

MethodsX, Journal Year: 2025, Volume and Issue: 14, P. 103232 - 103232

Published: Feb. 17, 2025

Language: Английский

---

Leveraging Mask Autoencoder and Crossover Binary Sand Cat Algorithm for Early Detection of Glaucoma

Microscopy Research and Technique, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 17, 2025

ABSTRACT Glaucoma, a leading cause of irreversible blindness worldwide, can be effectively managed if detected early. Glaucoma is directly associated with aging as it commonly occurs in people over the age 40 and elderly people. detection retinal fundus images typically involves utilizing image processing machine learning techniques. By leveraging advancements computer vision, robust automated system developed to assist ophthalmologists screening diagnosing glaucoma from images. Furthermore, vary significantly quality due factors like illumination variations, focus, artifacts. Ensuring consistent across different datasets acquisition devices essential for reliable detection. Addressing these challenges requires interdisciplinary collaboration between develop solutions glaucoma. Hence novel mask autoencoder‐based crossover binary sand cat (MA‐CBSC) algorithm proposed detect In this algorithm, autoencoder recognizes features indicating presence input used fine tune overall performance by selecting most appropriate escaped overfitting issues. Preprocessing steps such enhancement, filtering, data cleaning are applied extracted ROI purpose increasing enhancing visibility relevant extraction attributes namely optic disc, cup‐to‐disc ratio, bean‐pot cupping, vertical enlargement derived along some other features. work, crossover‐based optimization utilized hyperparameter tuning enhance efficiency MA‐CBSC method. Extensive experimental assessments conducted, comparing effectiveness algorithms Retinal Disease Classification dataset, Fundus Detection Data Dataset, Dataset. The results obtained method compared existing techniques DLCNN‐MGWO‐VW, FRCNN‐FKM, ML‐DCNN, DNN‐MSO show its superiority. Seven evaluation parameters assessing model including positive predictive value (PPV), accuracy, precision, F 1 score, sensitivity, recall, specificity. These measures that has more promising than methods 98.3% accuracy.

Language: Английский

---

Cloud-Based Diabetic Retinopathy Severity Recognition System Using Ensemble Deep Convolutional Neural Network Classifier Model

Lecture notes in networks and systems, Journal Year: 2024, Volume and Issue: unknown, P. 323 - 330

Published: Jan. 1, 2024

Language: Английский

---

Precise detection of diabetic retinopathy using adaptive remora optimization algorithm with deep adversarial approach

Multimedia Tools and Applications, Journal Year: 2024, Volume and Issue: unknown

Published: July 17, 2024

Language: Английский

---