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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Published on: November 30, 2022

早期色素斑点细分和分类来自虹膜细胞图像分析,具有可解释的深度学习和多类支持向量机器.

Amjad R Khan¹, Rabia Javed², Tariq Sadad³

¹Department of Information Systems, Prince Sultan University, Riyadh 66833, Saudi Arabia.

Biochemistry and cell biology = Biochimie et biologie cellulaire

|October 31, 2023

概括

这项研究引入了一种可解释的深度学习模型,用于早期虹膜色素点细分和分类,改善早期视网膜疾病的诊断和预防失明. 该模型准确分析虹膜细胞图像,性能优于现有方法.

关键词:

危害健康的风险虹膜细胞图像虹膜细胞图像预期寿命预期寿命颜色斑点颜色斑点颜色斑点视网膜疾病视网膜疾病

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科学领域:

眼科医生眼科眼科
计算机科学计算机科学
医疗成像医学成像

背景情况:

视网膜疾病影响全球数千人,需要早期诊断以预防失明.
虹膜斑点细分对于分析虹膜细胞图像至关重要,这些图像往往会因噪音和偏角而退化.
当前的细分方法与杂的,不合作的虹膜图像作斗争,缺乏效率.

研究的目的:

开发一种可解释的深度学习模型,用于精确的虹膜色素斑点细分和分类.
通过分析虹膜细胞图像来改善眼睛综合征的早期检测.
克服传统,昂贵和耗时的诊断过程的局限性.

主要方法:

开发了一个可解释的深度学习模型,与多类支持向量机器集成.
该模型在三个基准数据集上进行了训练和测试:MILE,UPOL和Eyes SUB.
虹膜细胞图像被分析为早期的色素斑点细分和分类.

主要成果:

拟议的模型在分类错误方面,与现有方法相比,表现优越.
该模型有效地定位了虹膜表面的微色素斑点.
在基准数据集上的实验结果证实了模型的准确性和有效性.

结论:

开发的可解释深度学习模型为早期虹膜色素点分析提供了有前途的方法.
这种方法可以帮助早期诊断视网膜疾病,并可能预防失明.
该模型在细分和分类虹膜色素斑点方面的有效性提高了诊断能力.