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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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转移学习启用方法用于眼部疾病的检测和分类.

Mahmood Ul Hassan1, Amin A Al-Awady1, Naeem Ahmed2

  • 1Department of Computer Skills, Deanship of Preparatory Year, Najran University, Najran, 61441 Kingdom of Saudi Arabia.

Health information science and systems
|June 13, 2024
PubMed
概括

新的深度学习模型Ocular Net能够准确地检测和分类白内障和绿内障等眼睛疾病,准确率为98.89%. 这一进步为各种眼部疾病提供了更好的诊断.

关键词:
医学成像医学成像眼部疾病 眼部疾病转移学习转移学习

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科学领域:

  • 眼科和医学成像学
  • 医疗保健中的人工智能
  • 计算机视觉用于疾病检测检测.

背景情况:

  • 眼部疾病带来了诊断和治疗方面的挑战.
  • 深度学习显示了眼科医学图像分析的前景.
  • 准确有效地检测眼部疾病对于患者的治疗结果至关重要.

研究的目的:

  • 推出Ocular Net,这是一个用于眼部疾病检测和分类的新型深度学习模型.
  • 为了评估眼睛网络在眼睛图像的大数据集上的性能.
  • 将Ocular Net的性能与眼部疾病诊断的现有方法进行比较.

主要方法:

  • 使用了6200张眼睛图像的数据集,其中70%用于培训,30%用于测试.
  • 开发了Ocular Net,包括转移学习,平均聚合,剪切的ReLU和泄漏的ReLU.
  • 采用数据增强技术来提高模型性能并防止过度拟合.

主要成果:

  • 眼睛网实现了98.89%的准确性,损失值为0.12%.
  • 与以前的方法相比,该模型在检测和分类眼部疾病方面表现优越.
  • 通过不同的培训/测试比率和参数来评估绩效.

结论:

  • 眼睛网是用于诊断眼部疾病的高度准确和高效的深度学习模型.
  • 该模型显示了提高临床眼病诊断的准确性和效率的巨大潜力.
  • 进一步的研究可以探索眼睛网在眼科中的更广泛应用.