Papers Diabetic Retinopathy Detection
“Diabetic Retinopathy Detection” 태그가 달린 논문 59편 · 필터 해제
Confidence-Uncertainty Boundary Calibration for Bayesian Deep Learning in Medical Image Analysis
In critical decision support systems based on medical imaging, the reliability of AI-assisted decision-making is as relevant as predictive accuracy. Although deep learning models have demonstrated significant accuracy, t…
Diabetic Retinopathy DetectionExplainable AI for Diabetic Retinopathy Detection Using Deep Learning with Attention Mechanisms and Fuzzy Logic-Based Interpretability
The task of weed detection is an essential element of precision agriculture since accurate species identification allows a farmer to selectively apply herbicides and fits into sustainable agriculture crop management. Thi…
Diabetic Retinopathy DetectionHybrid Deep Learning Framework for Enhanced Diabetic Retinopathy Detection: Integrating Traditional Features with AI-driven Insights
Diabetic Retinopathy (DR), a vision-threatening complication of Dia-betes Mellitus (DM), is a major global concern, particularly in India, which has one of the highest diabetic populations. Prolonged hyperglycemia damage…
Diabetic Retinopathy DetectionEnhancing Safety in Diabetic Retinopathy Detection: Uncertainty-Aware Deep Learning Models with Rejection Capabilities
Diabetic retinopathy (DR) is a major cause of visual impairment, and effective treatment options depend heavily on timely and accurate diagnosis. Deep learning models have demonstrated great success identifying DR from r…
Diabetic Retinopathy DetectionPerceptronCARE: A Deep Learning-Based Intelligent Teleophthalmology Application for Diabetic Retinopathy Diagnosis
Diabetic retinopathy is a leading cause of vision loss among adults and a major global health challenge, particularly in underserved regions. This study presents PerceptronCARE, a deep learning-based teleophthalmology ap…
Diabetic Retinopathy DetectionComputational EfficiencyResearch on Improving the High Precision and Lightweight Diabetic Retinopathy Detection of YOLOv8n
Early detection and diagnosis of diabetic retinopathy is one of the current research focuses in ophthalmology. However, due to the subtle features of micro-lesions and their susceptibility to background interference, ex-…
Diabetic Retinopathy DetectionDeploying and Evaluating Multiple Deep Learning Models on Edge Devices for Diabetic Retinopathy Detection
Diabetic Retinopathy (DR), a leading cause of vision impairment in individuals with diabetes, affects approximately 34.6% of diabetes patients globally, with the number of cases projected to reach 242 million by 2045. Tr…
Diabetic Retinopathy DetectionGPUVR-FuseNet: A Fusion of Heterogeneous Fundus Data and Explainable Deep Network for Diabetic Retinopathy Classification
Diabetic retinopathy is a severe eye condition caused by diabetes where the retinal blood vessels get damaged and can lead to vision loss and blindness if not treated. Early and accurate detection is key to intervention …
Diabetic Retinopathy DetectionDiagnosticImage EnhancementProgressive Transfer Learning for Multi-Pass Fundus Image Restoration
Diabetic retinopathy is a leading cause of vision impairment, making its early diagnosis through fundus imaging critical for effective treatment planning. However, the presence of poor quality fundus images caused by fac…
Diabetic Retinopathy DetectionImage RestorationTransfer LearningDiabetic Retinopathy Detection Based on Convolutional Neural Networks with SMOTE and CLAHE Techniques Applied to Fundus Images
Diabetic retinopathy (DR) is one of the major complications in diabetic patients' eyes, potentially leading to permanent blindness if not detected timely. This study aims to evaluate the accuracy of artificial intelligen…
Binary ClassificationDiabetic Retinopathy DetectionWavelet-based Global-Local Interaction Network with Cross-Attention for Multi-View Diabetic Retinopathy Detection
Multi-view diabetic retinopathy (DR) detection has recently emerged as a promising method to address the issue of incomplete lesions faced by single-view DR. However, it is still challenging due to the variable sizes and…
Diabetic Retinopathy DetectionObject Detection for Medical Image Analysis: Insights from the RT-DETR Model
Deep learning has emerged as a transformative approach for solving complex pattern recognition and object detection challenges. This paper focuses on the application of a novel detection framework based on the RT-DETR mo…
Diabetic Retinopathy DetectionMedical Image Analysisobject-detectionObject DetectionDiabetic Retinopathy Detection Using CNN with Residual Block with DCGAN
Diabetic Retinopathy (DR) is a major cause of blindness worldwide, caused by damage to the blood vessels in the retina due to diabetes. Early detection and classification of DR are crucial for timely intervention and pre…
Data AugmentationDiabetic Retinopathy DetectionGenerative Adversarial NetworkEnhancing Early Diabetic Retinopathy Detection through Synthetic DR1 Image Generation: A StyleGAN3 Approach
Diabetic Retinopathy (DR) is a leading cause of preventable blindness. Early detection at the DR1 stage is critical but is hindered by a scarcity of high-quality fundus images. This study uses StyleGAN3 to generate synth…
Diabetic Retinopathy DetectionImage GenerationEnhancing Transfer Learning for Medical Image Classification with SMOTE: A Comparative Study
This paper explores and enhances the application of Transfer Learning (TL) for multilabel image classification in medical imaging, focusing on brain tumor class and diabetic retinopathy stage detection. The effectiveness…
Brain Tumor ClassificationDiabetic Retinopathy Detectionimage-classificationImage Classification+4A Novel Adaptive Hybrid Focal-Entropy Loss for Enhancing Diabetic Retinopathy Detection Using Convolutional Neural Networks
Diabetic retinopathy is a leading cause of blindness around the world and demands precise AI-based diagnostic tools. Traditional loss functions in multi-class classification, such as Categorical Cross-Entropy (CCE), are …
Diabetic Retinopathy DetectionDiagnosticMulti-class ClassificationEnhancing Diabetic Retinopathy Detection with CNN-Based Models: A Comparative Study of UNET and Stacked UNET Architectures
Diabetic Retinopathy DR is a severe complication of diabetes. Damaged or abnormal blood vessels can cause loss of vision. The need for massive screening of a large population of diabetic patients has generated an interes…
Diabetic Retinopathy DetectionTraining Over a Distribution of Hyperparameters for Enhanced Performance and Adaptability on Imbalanced Classification
Although binary classification is a well-studied problem, training reliable classifiers under severe class imbalance remains a challenge. Recent techniques mitigate the ill effects of imbalance on training by modifying t…
Binary ClassificationDiabetic Retinopathy Detectionimbalanced classificationDiabetic Retinopathy Detection Using Quantum Transfer Learning
Diabetic Retinopathy (DR), a prevalent complication in diabetes patients, can lead to vision impairment due to lesions formed on the retina. Detecting DR at an advanced stage often results in irreversible blindness. The …
Diabetic Retinopathy DetectionQuantum Machine LearningTransfer LearningConformal Risk Control for Ordinal Classification
As a natural extension to the standard conformal prediction method, several conformal risk control methods have been recently developed and applied to various learning problems. In this work, we seek to control the confo…
ClassificationConformal PredictionDiabetic Retinopathy DetectionOrdinal Classification