Open Access

ARTICLE

Diabetic Retinopathy Diagnosis Using Interval Neutrosophic Segmentation with Deep Learning Model

by V. Thanikachalam^1,*, M. G. Kavitha², V. Sivamurugan¹

1 Department of Information Technology, Sri Sivasubramaniya Nadar College of Engineering, Chennai, 603110, India
2 Department of Computer Science and Engineering, University College of Engineering Pattukkottai, Rajamadam, 614701, India

* Corresponding Author: V. Thanikachalam. Email:

Computer Systems Science and Engineering 2023, 44(3), 2129-2145. https://doi.org/10.32604/csse.2023.026527

Received 29 December 2021; Accepted 16 March 2022; Issue published 01 August 2022

Abstract

In recent times, Internet of Things (IoT) and Deep Learning (DL) models have revolutionized the diagnostic procedures of Diabetic Retinopathy (DR) in its early stages that can save the patient from vision loss. At the same time, the recent advancements made in Machine Learning (ML) and DL models help in developing Computer Aided Diagnosis (CAD) models for DR recognition and grading. In this background, the current research works designs and develops an IoT-enabled Effective Neutrosophic based Segmentation with Optimal Deep Belief Network (ODBN) model i.e., NS-ODBN model for diagnosis of DR. The presented model involves Interval Neutrosophic Set (INS) technique to distinguish the diseased areas in fundus image. In addition, three feature extraction techniques such as histogram features, texture features, and wavelet features are used in this study. Besides, Optimal Deep Belief Network (ODBN) model is utilized as a classification model for DR. ODBN model involves Shuffled Shepherd Optimization (SSO) algorithm to regulate the hyperparameters of DBN technique in an optimal manner. The utilization of SSO algorithm in DBN model helps in increasing the detection performance of the model significantly. The presented technique was experimentally evaluated using benchmark DR dataset and the results were validated under different evaluation metrics. The resultant values infer that the proposed INS-ODBN technique is a promising candidate than other existing techniques.

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Keywords

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