IM-EDRD from Retinal Fundus Images Using Multi-Level Classification Techniques

Title

IM-EDRD from Retinal Fundus Images Using Multi-Level Classification Techniques

Creator

Karthikeyan M.P.; Mary Anita E.A.

Description

In recent years, there has been a significant increase in the number of people suffering from eye illnesses, which should be treated as soon as possible in order to avoid blindness. Retinal Fundus images are employed for this purpose, as well as for analysing eye abnormalities and diagnosing eye illnesses. Exudates can be recognised as bright lesions in fundus pictures, which can be the first indi-cator of diabetic retinopathy. With that in mind, the purpose of this work is to cre-ate an Integrated Model for Exudate and Diabetic Retinopathy Diagnosis (IM-EDRD) with multi-level classifications. The model uses Support Vector Machine (SVM)-based classification to separate normal and abnormal fundus images at the first level. The input pictures for SVM are pre-processed with Green Channel Extraction and the retrieved features are based on Gray Level Co-occurrence Matrix (GLCM). Furthermore, the presence of Exudate and Diabetic Retinopathy (DR) in fundus images is detected using the Adaptive Neuro Fuzzy Inference System (ANFIS) classifier at the second level of classification. Exudate detection, blood vessel extraction, and Optic Disc (OD) detection are all processed to achieve suitable results. Furthermore, the second level processing comprises Morphological Component Analysis (MCA) based image enhancement and object segmentation processes, as well as feature extraction for training the ANFIS clas-sifier, to reliably diagnose DR. Furthermore, the findings reveal that the proposed model surpasses existing models in terms of accuracy, time efficiency, and precision rate with the lowest possible error rate. 2023, Tech Science Press. All rights reserved.

Source

Intelligent Automation and Soft Computing, Vol-35, No. 1, pp. 567-580.

Date

2023-01-01

Publisher

Tech Science Press

Subject

ANFIS; diabetic retinopathy; exudate; morphological component analysis; Retinal fundus images; SVM

Coverage

Karthikeyan M.P., Department of Computer Science and Engineering, R.M.K. Engineering College, Chennai, 601206, India; Mary Anita E.A., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Bengaluru, 560029, India

Rights

All Open Access; Hybrid Gold Open Access

Relation

ISSN: 10798587

Format

Online

Language

English

Type

Article

Identifier

https://doi.org/10.32604/iasc.2023.026243

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85132209396&doi=10.32604%2fiasc.2023.026243&partnerID=40&md5=1c7e362cdd09dd6d173ec9fe514396c8