Automated diagnosis of diabetic retinopathy enabled by optimized thresholding-based blood vessel segmentation and hybrid classifier
| Autor: | Ganesh Shahubha Sable, Bakwad Kamlakar Murlidhar, Ajij Dildar Sayyad, Bansode Balbhim Narhari |
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| Rok vydání: | 2020 |
| Předmět: |
General Computer Science
business.industry Computer science 0211 other engineering and technologies Medicine (miscellaneous) Health Informatics Pattern recognition Vessel segmentation 02 engineering and technology Diabetic retinopathy medicine.disease Biochemistry Genetics and Molecular Biology (miscellaneous) Thresholding 0202 electrical engineering electronic engineering information engineering medicine 020201 artificial intelligence & image processing Artificial intelligence business Classifier (UML) 021101 geological & geomatics engineering |
| Zdroj: | Bio-Algorithms and Med-Systems. 17:9-23 |
| ISSN: | 1896-530X 1895-9091 |
| Popis: | Objectives The focus of this paper is to introduce an automated early Diabetic Retinopathy (DR) detection scheme from colour fundus images through enhanced segmentation and classification strategies by analyzing blood vessels. Methods The occurrence of DR is increasing from the past years, impacting the eyes due to a sudden rise in the glucose level of blood. All over the world, half of the people who are under age 70 are severely suffered from diabetes. The patients who are affected by DR will lose their vision during the absence of early recognition of DR and appropriate treatment. To decrease the growth and occurrence of loss of vision, the early detection and timely treatment of DR are desirable. At present, deep learning models have presented better performance using retinal images for DR detection. In this work, the input retinal fundus images are initially subjected to pre-processing that undergoes contrast enhancement by Contrast Limited Adaptive Histogram Equalization (CLAHE) and average filtering. Further, the optimized binary thresholding-based segmentation is done for blood vessel segmentation. For the segmented image, Tri-level Discrete Level Decomposition (Tri-DWT) is performed to decompose it. In the feature extraction phase, Local Binary Pattern (LBP), and Gray-Level Co-occurrence Matrices (GLCMs) are extracted. Next, the classification of images is done through the combination of two algorithms, one is Neural Network (NN), and the other Convolutional Neural Network (CNN). The extracted features are subjected to NN, and the tri-DWT-based segmented image is subjected to CNN. Both the segmentation and classification phases are enhanced by the improved meta-heuristic algorithm called Fitness Rate-based Crow Search Algorithm (FR-CSA), in which few parameters are optimized for attaining maximum detection accuracy. Results The proposed DR detection model was implemented in MATLAB 2018a, and the analysis was done using three datasets, HRF, Messidor, and DIARETDB. Conclusions The developed FR-CSA algorithm has the best detection accuracy in diagnosing DR. |
| Databáze: | OpenAIRE |
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