Abstract
Diabetic retinopathy (DR) detection is an emerging biometric modality which deserves a discussion and systematic review of the connected methods and findings. In this paper, most of the pattern recognition-based retinal blood vessels extraction techniques will be reviewed which have been applied to detect diabetic retinopathy (DR). In particular, we categorize the methodologies based on the extraction and segmentation techniques. Finally, a comparative analysis of a few of the pattern recognition-based DR detection techniques is presented on the basis of their characteristics and other parameters like sensitivity, specificity, and accuracy. The comparative study includes the cases where data collected from the publicly available datasets. The analysis shows that most of the techniques that have been proposed for DR detection perform well to extract wide and normal vessels from retinal images. However, few techniques cannot extract the tiny, thin, and abnormal vessels. As a result, performance degradation occurs. That notwithstanding, only a few of the proposed DR detection methods appear to be able to support performance improvement.
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Das, S., Majumder, S. (2022). A Review on Pattern Recognition-Based Retinal Blood Vessels Extraction Technique to Detect Diabetic Retinopathy (DR). In: Saraswat, M., Roy, S., Chowdhury, C., Gandomi, A.H. (eds) Proceedings of International Conference on Data Science and Applications. Lecture Notes in Networks and Systems, vol 287. Springer, Singapore. https://doi.org/10.1007/978-981-16-5348-3_5
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DOI: https://doi.org/10.1007/978-981-16-5348-3_5
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