Automated diagnosis of diabetic retinopathy enabled by optimized thresholding-based blood vessel segmentation and hybrid classifier

• Autorzy: Narhari Bansode Balbhim , Murlidhar Bakwad Kamlakar , Sayyad Ajij Dildar , Sable Ganesh Shahubha

Identyfikatory

DOI

10.1515/bams-2020-0053

• Języki publikacji: EN

Abstrakty

EN

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 triDWT-based segmented image is subjected to CNN. Both the segmentation and classification phases are enhanced by the improved meta-heuristic algorithm called Fitness Ratebased 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.

Słowa kluczowe

EN

average filtering; contrast limited adaptive histogram equalization; convolutional neural network; diabetic retinopathy; fitness rate-based crow search algorithm; gray-level co-occurrence matrices; neural network; optimized binary thresholding; tri-level discrete level decomposition

• Wydawca: Uniwersytet Jagielloński - Collegium Medicum ; Index Copernicus Sp. z o.o.
• Czasopismo: Bio-Algorithms and Med-Systems
• Rocznik: 2021
• Tom: Vol. 17, no. 1
• Strony: 9--23
• Opis fizyczny: Bibliogr. 37 poz., rys., tab.

Twórcy

autor

Narhari Bansode Balbhim

• Research Scholar, Department of Electronics & Telecommunication Engineering, MIT College of Engineering, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad, India

autor

Murlidhar Bakwad Kamlakar

• Department of Electronics Engineering, Puranmal Lahoti Govt. Polytechnic College, MSBTE, Latur, Mumbai, India

autor

Sayyad Ajij Dildar

• Department of Electronics & Telecommunication Engineering, MIT College of Engineering, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad, India

autor

Sable Ganesh Shahubha

• Department of Electronics & Telecommunication Engineering, MIT College of Engineering, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad, India

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).