Grey wolf optimization technique with U-shaped and capsule networks-A novel framework for glaucoma diagnosis

IF 1.6 Q2 MULTIDISCIPLINARY SCIENCES
MethodsX Pub Date : 2025-03-31 DOI:10.1016/j.mex.2025.103285
Govindharaj I , Ramesh T , Poongodai A , Senthilkumar K. P , Udayasankaran P , Ravichandran S
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引用次数: 0

Abstract

The worldwide prevalence of glaucoma makes it a major reason for blindness thus proper early diagnosis remains essential for preventing major vision deterioration. Current glaucoma screening methods that need expert handling prove to be time-intensive and complicated before yielding appropriate diagnosis and treatment. Our system addresses these difficulties through an automated glaucoma screening platform which combines advanced segmentation methods with classification approaches. A hybrid segmentation method combines Grey Wolf Optimization Algorithm with U-Shaped Networks to obtain precise extraction of the optic disc regions in retinal fundus images. Through GWOA the network achieves optimal segmentation by adopting wolf-inspired behaviors such as circular and jumping movements to identify diverse image textures. The glaucoma classification depends on CapsNet as a deep learning model that provides exceptional image detection to ensure precise diagnosis. The combination of our method delivers 96.01 % segmentation together with classification precision which outstrips traditional approaches while indicating strong capabilities for discovering glaucoma at early stages. This automated diagnosis system elevates clinical accuracy levels through an automated screening method that solves manual process limitations. The detection framework produces better accuracy to improve clinical results in a strong effort to minimize glaucoma-induced blindness worldwide and display its capabilities in real clinical environments.
  • Hybrid GWOA-UNet++ for precise optic disc segmentation.
  • CapsNet-based classification for robust glaucoma detection.
  • Achieved 96.01 % accuracy, surpassing existing methods.

Abstract Image

u型和胶囊网络的灰狼优化技术——青光眼诊断的新框架
青光眼在世界范围内的流行使其成为失明的主要原因,因此正确的早期诊断对于预防严重的视力恶化至关重要。目前需要专家处理的青光眼筛查方法在产生适当的诊断和治疗之前被证明是费时且复杂的。我们的系统通过自动化青光眼筛查平台解决了这些困难,该平台结合了先进的分割方法和分类方法。混合分割方法将灰狼优化算法与u形网络相结合,对视网膜眼底图像中的视盘区域进行精确提取。通过GWOA,网络采用狼启发的行为(如圆形和跳跃运动)来识别不同的图像纹理,从而实现最优分割。青光眼分类依赖于CapsNet作为一种深度学习模型,提供卓越的图像检测以确保精确诊断。该方法的分割率为96.01%,分类精度超过传统方法,在青光眼早期发现能力强。这种自动诊断系统通过解决人工过程限制的自动筛选方法提高了临床准确性水平。该检测框架具有更高的准确性,可以改善临床结果,在全球范围内最大限度地减少青光眼致盲,并在真实的临床环境中展示其能力。•混合gwoa - unnet++精确视盘分割。•基于capsnet的青光眼检测分类。•准确率达到96.01%,优于现有方法。
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来源期刊
MethodsX
MethodsX Health Professions-Medical Laboratory Technology
CiteScore
3.60
自引率
5.30%
发文量
314
审稿时长
7 weeks
期刊介绍:
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