Abstract
Diabetic retinopathy (DR) is a prevalent complication of diabetes mellitus that can lead to This paper presents a novel machine learning approach using TensorFlow for the automated detection and stage classification of diabetic retinopathy from retinal images. The study utilizes a comprehensive dataset of retinal images, which undergo preprocessing steps to enhance image quality and prepare them for analysis. A deep learning model based on convolutional neural networks (CNNs) is developed using TensorFlow, leveraging its efficient computational capabilities and optimization tools. The proposed model using Mobile Net is trained and validated on the retinal image dataset to classify diabetic retinopathy into different stages, ranging from mild to severe. Keywords— Deep Learning, Mobile Net, Diabetic Retinopathy (DR) , Dataset
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