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Retinal disease detection using deep learning techniques: a comprehensive review
S Muchuchuti, S Viriri - Journal of Imaging, 2023 - mdpi.com
Millions of people are affected by retinal abnormalities worldwide. Early detection and
treatment of these abnormalities could arrest further progression, saving multitudes from …
treatment of these abnormalities could arrest further progression, saving multitudes from …
Deep learning-based prediction of diabetic retinopathy using CLAHE and ESRGAN for enhancement
Vision loss can be avoided if diabetic retinopathy (DR) is diagnosed and treated promptly.
The main five DR stages are none, moderate, mild, proliferate, and severe. In this study, a …
The main five DR stages are none, moderate, mild, proliferate, and severe. In this study, a …
A comprehensive review of artificial intelligence models for screening major retinal diseases
This paper provides a systematic survey of artificial intelligence (AI) models that have been
proposed over the past decade to screen retinal diseases, which can cause severe visual …
proposed over the past decade to screen retinal diseases, which can cause severe visual …
Vision transformer model for predicting the severity of diabetic retinopathy in fundus photography-based retina images
Diabetic Retinopathy (DR) is a result of prolonged diabetes with poor blood sugar
management. It causes vision problems and blindness due to the deformation of the human …
management. It causes vision problems and blindness due to the deformation of the human …
Deep learning-enhanced diabetic retinopathy image classification
Objective Diabetic retinopathy (DR) can sometimes be treated and prevented from causing
irreversible vision loss if caught and treated properly. In this work, a deep learning (DL) …
irreversible vision loss if caught and treated properly. In this work, a deep learning (DL) …
Vision transformer with masked autoencoders for referable diabetic retinopathy classification based on large-size retina image
Computer-aided diagnosis systems based on deep learning algorithms have shown
potential applications in rapid diagnosis of diabetic retinopathy (DR). Due to the superior …
potential applications in rapid diagnosis of diabetic retinopathy (DR). Due to the superior …
Improved ResNet_101 assisted attentional global transformer network for automated detection and classification of diabetic retinopathy disease
S Karthika, M Durgadevi - Biomedical Signal Processing and Control, 2024 - Elsevier
An essential complication of the diabetes disease is termed as Diabetic Retinopathy (DR),
and it leads visual impairment in long-term vision. In general, DR is a kind of eye disease …
and it leads visual impairment in long-term vision. In general, DR is a kind of eye disease …
CTNet: convolutional transformer network for diabetic retinopathy classification
Currently, diabetic retinopathy diagnosis tools use deep learning and machine learning
algorithms for fundus image classification. Deep learning techniques especially convolution …
algorithms for fundus image classification. Deep learning techniques especially convolution …
Enhancing diabetic retinopathy classification using deep learning
Prolonged hyperglycemia can cause diabetic retinopathy (DR), which is a major contributor
to blindness. Numerous incidences of DR may be avoided if it were identified and …
to blindness. Numerous incidences of DR may be avoided if it were identified and …
An optimized deep-learning algorithm for the automated detection of diabetic retinopathy
Diabetic retinopathy (DR) leads to vision loss, a significant issue among people with
diabetes. The DR significantly impacts society's financial conditions, particularly in the …
diabetes. The DR significantly impacts society's financial conditions, particularly in the …