A Systematic Review of Fundus Image Analysis for Diagnosing Diabetic Retinopathy
Keywords:
Classification, Deep Learning, Diabetic Retinopathy, Fundus Images, SegmentationAbstract
Retinal image analysis reflects the rapid expansion of medical infrastructure, and efficient artificial intelligence models are created. Diabetic Retinopathy (DR), a disorder of the eyes caused by diabetes, is the most prevalent cause of vision loss in the eyes. To maintain vision, early diagnosis is essential. The manual diagnosing process used by ophthalmologists is difficult and time-consuming. Machine learning and Deep learning models based on artificial intelligence are crucial in raising the system’s accuracy. This survey discusses several recent approaches to image-preprocessing techniques, dataset descriptions, evaluation metrics, the backbone model for classification, and segmentation. Lesions such as hemorrhages, exudates, and microaneurysms in the Fundus images are identified using AI-based techniques for early DR diagnosis which prevent irreversible vision loss. To further categorize the severity of the disease, this survey includes pre-trained models for DR classification such as Alexnet, VGG, ResNet, DenseNet, and other models in addition to traditional CNN networks. Finally, challenges in the future scope are also addressed, which gives attention to the researcher for their future research.
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