A hybrid CNN and LSTM deep learning model achieved an accuracy of roughly 99.07% in distinguishing between healthy and diabetic retinopathy-affected fundus images.
Does a hybrid CNN-LSTM deep learning model accurately identify diabetic retinopathy from color fundus images?
Color fundus images from diabetic patients available in the MESSIDOR dataset
Hybrid deep learning model combining Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM)
Accuracy of distinguishing between healthy and DR-affected fundi
A hybrid CNN-LSTM deep learning model demonstrates high accuracy (99.07%) for the automated detection of diabetic retinopathy from fundus images.
Diabetes is a malnutrition that results from elevated glucose levels. Diabetes eventually results in diabetic retinopathy (DR), a retinal condition that significantly impairs vision. It is an insulin-dependent difficulty that impacts ocular health. The receptive to cells at the back of the individual retina's veins gets injured, which is the cause of it. A favorable prognosis for this condition depends on its early identification. In this paper, the application of Convolutional Neural Network (CNN) with Long Short Term Memory (LSTM) on color fundus images is used for diabetic retinopathy is applied for understanding the task. The goal of the proposed DR identification method is to use deep learning to automatically identify the problem. An hybrid model that was trained and tested to distinguish between healthy and DR-affected fundi was able to achieve an accuracy of roughly 99.07% using fundus images from diabetic patients that were available in MESSIDOR.
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Mary Vespa M
J. Shajeena
R M Shiny
SRM Institute of Science and Technology
Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology
St. Joseph's Institute of Technology
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M et al. (Tue,) conducted a other in Diabetic Retinopathy. Hybrid Convolutional Neural Network (CNN) with Long Short Term Memory (LSTM) vs. Healthy fundi was evaluated on Accuracy of distinguishing between healthy and DR-affected fundi. A hybrid CNN and LSTM deep learning model achieved an accuracy of roughly 99.07% in distinguishing between healthy and diabetic retinopathy-affected fundus images.
synapsesocial.com/papers/6a1d53b95a0c5c56ea04d8d9 — DOI: https://doi.org/10.1109/icears64219.2025.10940945
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