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深眼瘤分类模型使用子搜索算法和卡普托分数梯度下降.

Abduljlil Abduljlil Ali Abduljlil Habeb1, Ningbo Zhu1,2, Mundher Mohammed Taresh1

  • 1College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan, China.

PeerJ. Computer science
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PubMed
概括

一个新的深度学习优化器结合了卡普托的分数梯度下降和子搜索算法,显著提高了眼睛瘤分类的准确性和 fundus图像中的速度.

关键词:
卡普托的分数梯度下降.子搜索算法 子搜索算法深度学习是一种深度学习.眼睛的瘤是眼睛中的瘤.

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科学领域:

  • 眼科医生 眼科 眼科
  • 医疗成像医学成像
  • 人工智能的人工智能

背景情况:

  • 解释用于瘤诊断的数字眼底图像是具有挑战性的,因为图像的复杂性和微妙的瘤特征.
  • 自动检测眼部瘤对于及时诊断和有效治疗至关重要.

研究的目的:

  • 研究一种强大的深度学习系统,使用眼底图像对眼睛瘤进行分类.
  • 引入和评估一种新型的优化器,将卡普托分数梯度下降 (CFGD) 与子搜索算法 (CSA) 集成,以提高准确性和合速度.

主要方法:

  • 在400张眼底图像 (良性与恶性) 上训练Vgg16,AlexNet和GoogLeNet模型,使用拟议的CFGD-CSA优化器.
  • 将新型优化器的性能与SGDM,ADAM,CSA,CFGD,BASADAM和CSA-ADAM等现有方法进行了比较.
  • 基于准确性,稳定性,一致性和融合速度来评估性能.

主要成果:

  • 拟议的优化器实现了86.43% (Vgg16),87.42% (AlexNet) 和87.62% (GoogLeNet) 的平均准确率.
  • 与现有方法相比,在分类准确性,稳定性,一致性和融合速度方面取得了显著的改进.
  • 新型优化器显示了在医疗图像分类中改善深度学习模型性能的巨大潜力.

结论:

  • 与新型优化器一起开发的深度学习系统为准确的眼睛瘤识别提供了有希望的方法.
  • 这项研究有助于推进眼睛瘤的计算机辅助诊断系统,突出了优化器在医学图像分析中的好处.