Download PDFOpen PDF in browserClassification of Stages Diabetic Retinopathy Using MobileNetV2 Model11 pages•Published: January 16, 2022AbstractDiabetic retinopathy (DR) is a complication of diabetes mellitus that causes retinal damage that can lead to vision loss if not detected and treated promptly. The common diagnosis stages of the disease take time, effort, and cost and can be misdiagnosed. In the recent period with the explosion of artificial intelligence, deep learning has become the most popular tool with high performance in many fields, especially in the analysis and classification of medical images. The Convolutional Neural Network (CNN) is more widely used as a deep learning method in medical imaging analysis with highly effective. In this paper, the five-stage image of modern DR (healthy, mild, moderate, severe, and proliferative) can be detected and classified using the deep learning technique. After cross-validation training and testing on the corresponding 5,590-image dataset, a pre-MobileNetV2 training model is proposed in classifying stages of diabetic retinopathy. The average accuracy of the model achieved was 93.89% with the precision of 94.00%, recall 92.00% and f1-score 90.00%. The corresponding thermal image is also given to help experts for evaluating the influence of the retina in each different stage.Keyphrases: balancing dataset, deep learning, diabetic retinopathy, mobilenetv2 In: Tich Thien Truong, Trung Nghia Tran, Thanh Nha Nguyen and Quoc Khai Le (editors). Proceedings of International Symposium on Applied Science 2021, vol 4, pages 147-157.
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