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Apr 17, 2024 · By comparing medical images of patients at different time points, changes in lesions can be detected and quantified, providing more accurate ...
By comparing retinal images from multiple time points, changes in diabetic retinopathy can be detected, providing key information about disease progression. In ...
May 17, 2023 · Deep learning methods have been developed to detect DME from two-dimensional retinal images and also from optical coherence tomography (OCT) images.
This research article presents a novel method for DR detection, which is based on transfer learning to detect and classify DR lesions accurately.
Jan 25, 2023 · We developed a deep learning system (DLS) that detects referable DR from retinal images acquired using handheld non-mydriatic fundus camera by non-technical ...
Mar 4, 2024 · In this study, a deep learning approach is proposed to classify DR fundus images by severity levels, utilizing GoogleNet and ResNet models ...
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In this paper, an improved activation function was proposed for diagnosing DR from fundus images that automatically reduces loss and processing time.
May 28, 2021 · We develop a deep learning system, named DeepDR, that can detect early-to-late stages of diabetic retinopathy.
Jan 4, 2023 · In this research, a deep learning network is used to automatically detect and classify DR fundus images depending on severity using AlexNet and Resnet101-based ...
This study describes a computer-aided screening system (DREAM) that uses a neural network classification model in machine learning to assess fundus images.