Open Access

ARTICLE

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

M. P. Karthikeyan^1,*, E. A. Mary Anita²

1 Department of Computer Science and Engineering, R.M.K. Engineering College, Chennai, 601206, India
2 Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Bengaluru, 560029, India

* Corresponding Author: M. P. Karthikeyan. Email:

Intelligent Automation & Soft Computing 2023, 35(1), 567-580. https://doi.org/10.32604/iasc.2023.026243

Received 20 December 2021; Accepted 17 February 2022; Issue published 06 June 2022

Abstract

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 indicator of diabetic retinopathy. With that in mind, the purpose of this work is to create 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 classifier, 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.

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