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深眼:改进了使用自我注意力和密集层识别眼部疾病的转移学习架构

Qaisar Abbas¹, Mubarak Albathan¹, Abdullah Altameem¹

¹College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia.

Diagnostics (Basel, Switzerland)

|October 28, 2023

概括

一个人工智能系统使用视网膜图像准确地检测早期的眼睛疾病,如玻璃眼和糖尿病视网膜病变. 这种深度学习模型有助于临床医生及时诊断和查,改善患者的治疗结果.

科学领域:

眼科医生眼科眼科
计算机科学计算机科学
人工智能的人工智能

背景情况:

对于临床医生来说,早期检测眼睛疾病,如玻璃眼 (GA),糖尿病视网膜病变 (DR) 和白内障 (CT) 是一个挑战,往往导致劳动密集型和容易出错的手动诊断.
眼部疾病的多样性需要自动化系统和计算机辅助诊断 (CAD) 工具来准确识别疾病.
深度学习 (DL) 算法在提高用于疾病检测的视网膜图像分类准确度方面表现有前途.

研究的目的:

开发一种智能,自动化的眼部疾病检测系统,利用视网膜底部图像的深度学习.
通过创建眼睛疾病的计算机辅助诊断 (CAD) 工具来解决手动诊断的局限性.
通过先进的组合模型,提高诊断各种眼病的准确性和效率.

主要方法:

开发了一个三步混合组合模型,集成特征提取,选择和分类.
改进的AlexNet (iAlexNet) 架构与注意力和密集层被用于从视网膜图像中提取增强的特征.
使用ReliefF方法来选择特征以减少维度,然后使用XgBoost分类器进行分类.

主要成果:

开发的基于AlexNet-ReliefF-XgBoost架构的深眼模型实现了95.13%的高精度.
数据增强技术成功地用于管理ODIR和RFMiD数据集中的类不平衡问题.
整体模型在从视网膜底部图像分类不同类型的眼部疾病方面表现出卓越的性能.

结论:

拟议的混合组合模型为自动眼部疾病检测提供了强大而准确的解决方案.
这种基于深度学习的系统可以显著帮助临床医生,包括经验较少的眼科医生,在早期诊断和查与眼睛有关的疾病.
这些发现突出了先进的人工智能技术在提高眼科诊断的效率和可靠性的潜力.

关键词:

亚历克斯的网络亚历克斯的网络在XgBoost中使用XgBoost.白内障是什么?白内障是什么?白内障是什么?计算机辅助诊断是指计算机辅助的诊断.深度学习是一种深度学习.糖尿病视网膜病变糖尿病视网膜病变玻璃眼 glaucoma 玻璃眼玻璃眼玻璃眼玻璃眼多个标签的分类.眼部疾病眼部疾病转移学习转移学习

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