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A Detailed Systematic Review on Retinal Image Segmentation Methods

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Abstract

The separation of blood vessels in the retina is a major aspect in detecting ailment and is carried out by segregating the retinal blood vessels from the fundus images. Moreover, it helps to provide earlier therapy for deadly diseases and prevent further impacts due to diabetes and hypertension. Many reviews already exist for this problem, but those reviews have presented the analysis of a single framework. Hence, this article on retinal segmentation review has revealed distinct methodologies with diverse frameworks that are utilized for blood vessel separation. The novelty of this review research lies in finding the best neural network model by comparing its efficiency. For that, machine learning (ML) and deep learning (DL) were compared and have been reported as the best model. Moreover, different datasets were used to segment the retinal blood vessels. The execution of each approach is compared based on the performance metrics such as sensitivity, specificity, and accuracy using publically accessible datasets like STARE, DRIVE, ROSE, REFUGE, and CHASE. This article discloses the implementation capacity of distinct techniques implemented for each segmentation method. Finally, the finest accuracy of 98% and sensitivity of 96% were achieved for the technique of Convolution Neural Network with Ranking Support Vector Machine (CNN-rSVM). Moreover, this technique has utilized public datasets to verify efficiency. Hence, the overall review of this article has revealed a method for earlier diagnosis of diseases to deliver earlier therapy.

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  1. Department of Electronics and Communication Engineering, Silicon Institute of Technology, Bhubaneswar, Orissa, 751024, India

    Nihar Ranjan Panda

  2. Department of Electronics and Communication Engineering, National Institute of Technology, Rourkela, Odisha, 769008, India

    Ajit Kumar Sahoo

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Panda, N.R., Sahoo, A.K. A Detailed Systematic Review on Retinal Image Segmentation Methods. J Digit Imaging 35, 1250–1270 (2022). https://doi.org/10.1007/s10278-022-00640-9

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