Blood Vessel Segmentation and Classification for Diabetic Retinopathy Grading Using Dandelion Optimization Algorithm with Deep Learning Model

Abstract

Diabetic retinopathy (DR) is a diabetic complexity that mainly affects the eye. Generally, an ophthalmologist defines the severity of the retinopathy by directly inspecting colour images and estimating them by visually examining the fundus. Due to the enormous amount of diabetic patients all over the world, it becomes an expensive process. The automated system was designed for accurate recognition of the disease using segmentation and fundus image. The blood vessel segmentation process is used to identify and separate blood vessels from the surrounding tissues in an image. This is a crucial stage in the detection of DR, as it enables physicians to measure and identify changes in blood vessels that are indicative of the disease. Numerous approaches are used for blood vessel segmentation and DR classification, along with deep learning (DL) and machine learning (ML) techniques. In this article, we introduce a novel dandelion optimization algorithm with deep learning based blood vessel segmentation and classification (DOADL-BVSC) model for DR grading. The presented DOADL-BVSC technique involves the concepts of blood vessel segmentation and DL-based classification for DR diagnosis on retinal fundus photographs. To accomplish this, the presented DOADL-BVSC technique uses the fuzzy set type-II approach for the image enhancement process. Next, U-Net with Bi-directional feature pyramid network (U-BFPN) model is exploited to segment the blood vessels in the retinal imaging effectively. Moreover, a squeeze and excitation (SE) network is applied for feature vector generation with DOA based hyperparameter optimizer. Finally, quantum autoencoder (QAE) approach is utilized for identifying and classifying DR into distinct stages. The experimental analysis of the DOADL-BVSC approach is investigated on benchmark DR datasets. The simulation results depicted the superior results of the DOADL-BVSC technique with increased accuracy of 99.93%.

Migration-Crossover Algorithm: A Swarm-based Metaheuristic Enriched with Crossover Technique and Unbalanced Neighbourhood Search

“…There are many optimization studies utilized the metaheuristics to meet their objectives. In the biomedical system, dandelion optimization algorithm has been embedded to classify blood vessels for grading the diabetic retinopathy [1] while pelican optimization has been utilized to detect and classify tuberculosis based on the x-ray image of the chest [2]. In the agricultural sector, the red deer algorithm has been utilized to detect and classify plant diseases in the early season phase [3].…”

“…Many of the swarm-based metaheuristics exploit the practices of animals during breeding or tracing for prey or food, like Komodo mlipir algorithm (KMA) [5], green anaconda optimization (GAO) [6], walrus optimization algorithm (WaOA) [7], reptile search algorithm (RSA) [8], coati optimization algorithm (COA) [9], zebra optimization algorithm (ZOA) [10], golden jackal optimization (GJO) [11], chameleon swarm algorithm (CSA) [12], cat and mouse based optimization (CMBO) [13], clouded leopard optimization (CLO) [14], northern goshawk optimization (NGO) [15], pelican optimization algorithm (POA) [16], snow leopard optimization (SLO) [17], red fox optimization (RFO) [18], Siberian tiger optimization (STO) [19], white shark International Journal of Intelligent Engineering and Systems, Vol.17, No. 1,2024 DOI: 10.22266/ijies2024.0229.59 optimization (WSO) [20], Tasmanian devil optimization (TDO) [21], osprey optimization algorithm (OOA) [22], and so on. Some metaheuristics use their main reference during the directed search for their name such as random selected leader-based optimization (RSLBO) [23], multi leader optimization (MLO) [24], mixed leader-based optimization (MLBO) [25], hybrid leader-based optimization (HLBO) [26], three influential member-based optimizations (TIMBO) [27], and so on.…”