Open AccessClassification of Stages Diabetic Retinopathy Using MobileNetV2 Model
Author(s) -
Hoang Nhut Huynh,
Minh Thanh,
Gia Thinh Huynh,
Anh Tu Tran,
Trung Tran
Publication year - 2022
Publication title -
kalpa publications in engineering
Language(s) - English
Resource type - Conference proceedings
ISSN - 2515-1770
DOI - 10.29007/h46n
Subject(s) - diabetic retinopathy , deep learning , convolutional neural network , artificial intelligence , computer science , retinopathy , stage (stratigraphy) , diabetes mellitus , contextual image classification , artificial neural network , image (mathematics) , pattern recognition (psychology) , machine learning , medicine , paleontology , biology , endocrinology
Diabetic 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.