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Supervised by Ministry of Industry and Information Technology of The People's Republic of China Sponsored by Harbin Institute of Technology Editor-in-chief Yu Zhou ISSNISSN 1005-9113 CNCN 23-1378/T

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Related citation:G.Indira Devi.Detection and Classification of Diabetic Retinopathy ThroughIdentification of Blood Vessel Thickness Using FOFF& ML Classifiers[J].Journal of Harbin Institute Of Technology(New Series),2024,31(6):84-96.DOI:10.11916/j.issn.1005-9113.2024039.
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Detection and Classification of Diabetic Retinopathy ThroughIdentification of Blood Vessel Thickness Using FOFF& ML Classifiers
Author NameAffiliation
G.Indira Devi Electronics and Communication Engineering, Anil Neerukonda Institute of Technology and Science, Visakhapatnam 531162, Andhra Pradesh, India 
Abstract:
Diabetes is a significant issue in the medical field. The detection and identification of the human eye diseases caused by excessive blood sugar levels in diabetes mellitus are important. The main objective of this study is to propose a viable solution for diagnosis using fundus images. This study presents a stage by stage implementation methodology. The original fundus image is first preprocessed, then the blood vessels are segmented, and finally the features are extracted and classified. This work uses an effective way to introduce a meta-heuristic algorithm. Blood Vessel Segmentation (BVS) is vital in DR(Diabetic Retinopathy) detection; hence, this research proposes a Firefly-Optimized Frangi based Filter (FOFF). Categorizing the disease is the last procedure. The classifier K-Nearest Neighbour (KNN) has an accuracy of 91.62%, while the SVM does well with an accuracy score of 95.54%.
Key words:  diabetic retinopathy  firefly algorithm  optimized Frangi filter  KNN  SVM
DOI：10.11916/j.issn.1005-9113.2024039
Clc Number:TP391.4,R774.1
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