A systematic review on diabetic retinopathy detection using deep learning techniques
Segmentation is an essential requirement to accurately access diabetic retinopathy (DR)
and it becomes extremely time-consuming and challenging to detect manually. As a result …
and it becomes extremely time-consuming and challenging to detect manually. As a result …
A systematic literature review of machine learning based risk prediction models for diabetic retinopathy progression
Diabetic Retinopathy (DR) is the most popular debilitating impairment of diabetes and it
progresses symptom-free until a sudden loss of vision occurs. Understanding the …
progresses symptom-free until a sudden loss of vision occurs. Understanding the …
[HTML][HTML] Diabetic retinopathy identification using parallel convolutional neural network based feature extractor and ELM classifier
Diabetic retinopathy (DR) is an incurable retinal condition caused by excessive blood sugar
that, if left untreated, can result in even blindness. A novel automated technique for DR …
that, if left untreated, can result in even blindness. A novel automated technique for DR …
Grading diabetic retinopathy using multiresolution based CNN
Diabetic Retinopathy (DR) refers to a medical condition that affects the eye; it occurs due to
diabetes, and, if not detected early on, results in a reduction of visual capacity and may even …
diabetes, and, if not detected early on, results in a reduction of visual capacity and may even …
Image quality-aware diagnosis via meta-knowledge co-embedding
Medical images usually suffer from image degradation in clinical practice, leading to
decreased performance of deep learning-based models. To resolve this problem, most …
decreased performance of deep learning-based models. To resolve this problem, most …
A foundation language-image model of the retina (flair): Encoding expert knowledge in text supervision
Foundation vision-language models are currently transforming computer vision, and are on
the rise in medical imaging fueled by their very promising generalization capabilities …
the rise in medical imaging fueled by their very promising generalization capabilities …
mixDA: mixup domain adaptation for glaucoma detection on fundus images
Deep neural network has achieved promising results for automatic glaucoma detection on
fundus images. Nevertheless, the intrinsic discrepancy across glaucoma datasets is …
fundus images. Nevertheless, the intrinsic discrepancy across glaucoma datasets is …
Structure-Oriented Transformer for retinal diseases grading from OCT images
Retinal diseases are the leading causes of vision temporary or permanent loss. Precise
retinal disease grading is a prerequisite for early intervention or specific therapeutic …
retinal disease grading is a prerequisite for early intervention or specific therapeutic …
Contrastive self-supervised learning for diabetic retinopathy early detection
J Ouyang, D Mao, Z Guo, S Liu, D Xu… - Medical & Biological …, 2023 - Springer
Diabetic Retinopathy (DR) is the major cause of blindness, which seriously threatens the
world's vision health. Limited medical resources make early diagnosis and a large-scale …
world's vision health. Limited medical resources make early diagnosis and a large-scale …
Diabetic retinopathy grading review: Current techniques and future directions
Diabetic retinopathy (DR) is widely recognized as a leading cause of blindness among
individuals with diabetes worldwide. Therefore, early diagnosis of DR plays a crucial role in …
individuals with diabetes worldwide. Therefore, early diagnosis of DR plays a crucial role in …