Comprehensive Survey of the State-of-the-Art Deep Learning Models for Diabetic Retinopathy Detection and Grading Using Retinal Fundus Photography
DOI:
https://doi.org/10.29194/NJES.27020155Keywords:
Diabetic Retinopathy, CNN models, DR Datasets ClassificationAbstract
In order to avoid losing sense of sight in a large portion of the working population, Diabetic Retinopathy (DR) identification during broad examination for diabetes is crucial. To prevent blindness in the future, early illness detection and measurement of disease development are essential. DR is diagnosed through medical image analysis. After the success of Deep Learning (DL) in other applications in the real world, it is considered a vital tool for upcoming health sector applications, providing solutions with accurate results for medical image analysis. This review provides a comprehensive survey of the state-of-the-art DL models for DR detection and grading using retinal fundus photography. This review thoroughly examined and summarized 81 relevant publications that were published through IEEE Xplore, Web of Science, PubMed, and Scopus between 2018 and 2023 based on the available database with binary or multiclass CNN classification models as well as the main preprocessing techniques. According to the findings of this review, transfer learning has proven to be an excellent technique for addressing the problems of limited resources for data for DR analysis. CNN models having tens or hundreds of layers are the most frequently utilized frameworks for DR classification. The most extensively utilized datasets for DR categorization are Aptos 2019 and EyePACS. Although DL has attained or surpassed human-level DR classification accuracy, there is still more work to be done in real-world clinical procedures.
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