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180 results on '"Fundus image"'

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1. VISTA: vision improvement via split and reconstruct deep neural network for fundus image quality assessment.

2. Assessing the Correlation Between Retinal Arteriolar Bifurcation Parameters and Coronary Atherosclerosis.

3. A Dual-Modal Fusion Network Using Optical Coherence Tomography and Fundus Images in Detection of Glaucomatous Optic Neuropathy.

4. Diagnosis and multiclass classification of diabetic retinopathy using enhanced multi thresholding optimization algorithms and improved Naive Bayes classifier.

5. Validation of neuron activation patterns for artificial intelligence models in oculomics.

6. Multi-classification of eye disease based on fundus images using hybrid Squeeze Net and LRCN model.

7. Evaluating the potential of retinal photography in chronic kidney disease detection: a review.

8. Automatic Exudate Detection from Retinal Fundus Images in Diabetic Retinopathy.

9. Earlier smart prediction of diabetic retinopathy from fundus image under innovative ResNet optimization maneuver.

10. A Survey on Classifying Ocular Diseases Using Deep Learning and Machine Learning Techniques.

11. Modified 2D-EWT-based Automated System for Glaucoma Diagnosis.

12. Hybrid technique for fundus image enhancement using modified morphological filter and denoising net.

13. An attention enriched encoder–decoder architecture with CLSTM and RES unit for segmenting exudate in retinal images.

14. 基于改进YOLOv5的眼底出血点检测算法.

15. UGLS: an uncertainty guided deep learning strategy for accurate image segmentation.

16. A systematic review on diabetic retinopathy detection and classification based on deep learning techniques using fundus images.

17. Ophthalmic Diseases Classification Based on YOLOv8.

18. Manhattan Vision Screening and Follow-Up Study: (NYC-SIGHT)Tele-Retinal Image Findings and Importance of Photography.

19. Automated Diabetic Retinopathy Grading based on the Modified Capsule Network Architecture.

20. Retinal fundus image classification for diabetic retinopathy using transfer learning technique.

21. An early-stage diagnosis of diabetic retinopathy based on ensemble framework.

22. KHCM Fundus photograph-based cataract evaluation network using deep learning.

23. Retinal Disease Diagnosis Using Deep Learning on Ultra-Wide-Field Fundus Images.

24. Resilient back-propagation machine learning-based classification on fundus images for retinal microaneurysm detection.

25. Detection and Classification of Diabetic Retinopathy Using Inception V3 and Xception Architectures.

26. Comparing the Robustness of ResNet, Swin-Transformer, and MLP-Mixer under Unique Distribution Shifts in Fundus Images.

27. Intelligent identification and classification of diabetic retinopathy using fuzzy inference system.

29. Retina disease prediction using modified convolutional neural network based on Inception‐ResNet model with support vector machine classifier.

30. Automated Glaucoma Detection in Retinal Fundus Images Using Machine Learning Models.

31. A High-Resolution Network with Strip Attention for Retinal Vessel Segmentation.

32. Detection and location of microaneurysms in fundus images based on improved YOLOv4 with IFCM.

33. A Straightforward Bifurcation Pattern-Based Fundus Image Registration Method.

34. An effective and comprehensible method to detect and evaluate retinal damage due to diabetes complications.

35. Quantitative analysis of Fundus Image Enhancement in the Detection of Diabetic Retinopathy Using Deep Convolutional Neural Network.

36. Comparison of different measuring methods in the assessment of the ISNT rule and its variants in a normal population: A cross‐sectional study.

37. CLRD: Collaborative Learning for Retinopathy Detection Using Fundus Images.

38. MIL-CT: Multiple Instance Learning via a Cross-Scale Transformer for Enhanced Arterial Light Reflex Detection.

39. Detection of Diabetic Retinopathy using Convolutional Neural Networks for Feature Extraction and Classification (DRFEC).

40. Automatic Detection and Classification of Diabetic Retinopathy Using the Improved Pooling Function in the Convolution Neural Network.

41. Diabetic retinopathy detection by optimized deep learning model.

42. Supervised Contrastive Learning with Angular Margin for the Detection and Grading of Diabetic Retinopathy.

43. Contextual Augmentation Based on Metric-Guided Features for Ocular Axial Length Prediction.

44. Separation of arteries and veins in retinal fundus images with a new CNN architecture.

45. Ocular disease examination of fundus images by hybriding SFCNN and rule mining algorithms.

46. A novel automated komodo Mlipir optimization-based attention BiLSTM for early detection of diabetic retinopathy.

47. Multi-stage glaucoma classification using pre-trained convolutional neural networks and voting-based classifier fusion.

48. Prediction of diabetic retinopathy using machine learning techniques.

49. Retinal image‐based artificial intelligence in detecting and predicting kidney diseases: Current advances and future perspectives.

50. XCapsNet: A deep neural network for automated detection of diabetic retinopathy.

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