A ROBUST TECHNIQUES OF ENHANCEMENT AND SEGMENTATION BLOOD VESSELS IN RETINAL IMAGE USING DEEP LEARNING

Erwin, Erwin (2022) A ROBUST TECHNIQUES OF ENHANCEMENT AND SEGMENTATION BLOOD VESSELS IN RETINAL IMAGE USING DEEP LEARNING. Biomedical Engineering: Applications, Basis and Communications, 2022 (225001). pp. 1-9. ISSN 1793-7132 (In Press)

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Abstract

The retina is the most important part of the eye. Early detection of retinal disease can be done through the passage of the blood vessels of the retina. Enhancement of the quality of retinal images that have both noise and noise is the first step in image processing to help improve the accuracy of the results for image segmentation and extraction. Images store a lot of information, but often there is a decrease in quality or image defects. So that images that have experienced interference or noise are easily interpreted, then the image can be manipulated into other images of better quality using image processing techniques or methods. The neural network-based method that is currently popular is deep learning. The segmentation process is currently a widely used method of deep learning that has grown rapidly used in various studies. One of the popular methods is Convolutional Neural Network (CNN). CNN can handle large-dimensional data such as images because the input to CNN is in the form of a matrix. Since the findings of retinal blood vessel segmentation are often inaccurate and there is always noise, this study will look at how to segment retinal images in blood vessels using CNN U-Net and LadderNet methods. Proper segmentation of retinal blood vessels can be the first step to detecting a disease. Segmentation and analysis of retinal blood vessels can assist medical personnel in detecting the severity of a disease. The stages of image enhancement used are Histogram Equalization and Clahe. Segmentation of blood vessels is done using CNN U-Net and LadderNet Methods. The results of the application of the enhancement and segmentation using the U-Net and LadderNet methods on training and on testing data were tested on the DRIVE dataset. The results of measurement of accuracy, specificity, sensitivity and F1 Score of blood vessel segmentation using the U-Net CNN method were 95.46%, 98.56%, 74.20%, and 80.63%, respectively. While the results of the CNN LadderNet method were 95.47%, 98.42%, 75.19%, and 80.86%, respectively. Based on the results of blood vessel segmentation from two proposed methods, the result of the CNN LaddetNet method is greater than the CNN U-Net method in accuracy, sensitivity, and F1 Score. The proposed approach will be further developed in the future, with the aim of increasing the value of the blood vessel segmentation process evaluation outcomes.

Item Type: Article
Subjects: Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation.
Divisions: 09-Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User: Dr Erwin Erwin
Date Deposited: 17 Aug 2022 21:49
Last Modified: 17 Aug 2022 21:49
URI: http://repository.unsri.ac.id/id/eprint/77271

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