ArticleComputational intelligence and neuroscience2022
Enhancement of Detection of Diabetic Retinopathy Using Harris Hawks Optimization with Deep Learning Model.
Article in Computational intelligence and neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.
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Who cites it
28 citing papers in PubMed, 1 synthesis or guideline pooled it, 99 citations in OpenAlex.
- Virtual Reality and Cardiac Diseases: A Systematic Review of Applications and Effects.Journal of healthcare engineering · 2023Pooled it
- A Nomogram for Predicting Cardiovascular Diseases in Chronic Obstructive Pulmonary Disease Patients.Journal of healthcare engineering · 2022Trial
- A systematic review on deep learning techniques for diabetic retinopathy classification in retinal fundus images.iScience · 2026Article
- A novel diabetic retinopathy detection from fundus images using hybrid quantum convolutional neural network models.Scientific reports · 2026Article
- Scalable HMO-CNN-SVM Framework for Skin Lesion Classification: A Metaheuristic-Driven Approach With Parallelizable Optimization for Cluster Deployment.Biomedical engineering and computational biology · 2026Article
- HyperGraph-based capsule temporal memory network for efficient and explainable diabetic retinopathy detection in retinal imaging.Scientific reports · 2025Article
- Attention dual transformer with adaptive temporal convolutional for diabetic retinopathy detection.Scientific reports · 2025Article
- Chitosan as a promising materials for the construction of nanocarriers for diabetic retinopathy: an updated review.Journal of biological engineering · 2024Review
- A novel fusion of genetic grey wolf optimization and kernel extreme learning machines for precise diabetic eye disease classification.PloS one · 2024Article
- DR-NASNet: Automated System to Detect and Classify Diabetic Retinopathy Severity Using Improved Pretrained NASNet Model.Diagnostics (Basel, Switzerland) · 2023Article
- Enhancement of Diabetic Retinopathy Prognostication Using Deep Learning, CLAHE, and ESRGAN.Diagnostics (Basel, Switzerland) · 2023Article
- Investigating the Effectiveness of Novel Support Vector Neural Network for Anomaly Detection in Digital Forensics Data.Sensors (Basel, Switzerland) · 2023Article
- Analysis of the Effectiveness of Metaheuristic Methods on Bayesian Optimization in the Classification of Visual Field Defects.Diagnostics (Basel, Switzerland) · 2023Article
- Appropriate noise addition to metaheuristic algorithms can enhance their performance.Scientific reports · 2023Article
- A Survey on Deep-Learning-Based Diabetic Retinopathy Classification.Diagnostics (Basel, Switzerland) · 2023Review
- Clinical Decision Support System for Diabetic Patients by Predicting Type 2 Diabetes Using Machine Learning Algorithms.Journal of healthcare engineering · 2023Article
- An improved butterfly optimization algorithm for training the feed-forward artificial neural networks.Soft computing · 2023Article
- A Method of Deep Learning Model Optimization for Image Classification on Edge Device.Sensors (Basel, Switzerland) · 2022Article
- Diagnosis of Brain Tumor Using Light Weight Deep Learning Model with Fine-Tuning Approach.Computational and mathematical methods in medicine · 2022Article
- IOT-Based Medical Informatics Farming System with Predictive Data Analytics Using Supervised Machine Learning Algorithms.Computational and mathematical methods in medicine · 2022Article
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In today's world, diabetic retinopathy is a very severe health issue, which is affecting many humans of different age groups. Due to the high levels of blood sugar, the minuscule blood vessels in the retina may get damaged in no time and further may lead to retinal detachment and even sometimes lead to glaucoma blindness. If diabetic retinopathy can be diagnosed at the early stages, then many of the affected people will not be losing their vision and also human lives can be saved. Several machine learning and deep learning methods have been applied on the available data sets of diabetic retinopathy, but they were unable to provide the better results in terms of accuracy in preprocessing and optimizing the classification and feature extraction process. To overcome the issues like feature extraction and optimization in the existing systems, we have considered the Diabetic Retinopathy Debrecen Data Set from the UCI machine learning repository and designed a deep learning model with principal component analysis (PCA) for dimensionality reduction, and to extract the most important features, Harris hawks optimization algorithm is used further to optimize the classification and feature extraction process. The results shown by the deep learning model with respect to specificity, precision, accuracy, and recall are very much satisfactory compared to the existing systems.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.