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基于YOLOv8的5个相关皮肤镜结构的自动定位,以改善诊断

Esther Chabi Adjobo^1,2, Amadou Tidjani Sanda Mahama^1,2, Pierre Gouton¹

¹Imagerie et Vision Artificielle (ImVia), University of Bourgogne Franche-Comté, 21078 Dijon, France.

Journal of imaging

|July 28, 2023

概括

这项研究介绍了yolol-dermoscopic-features,一种人工智能算法,可以精确地检测皮肤损伤图像中的微妙皮肤学特征. 该方法显著改进了现有技术,帮助皮肤科医生进行诊断.

科学领域:

皮肤病学皮肤病学
医疗成像医学成像
人工智能的人工智能

背景情况:

皮肤学特征的自动检测有助于专家理解皮肤病变图像.
目前用于皮肤镜特征自动分析的方法因特征大小而面临挑战,需要改进.

研究的目的:

为了提高自动皮肤镜特征检测的精度.
为了解决目前皮肤学图像的自动化分析的局限性.

主要方法:

开发了一种新的算法,Yolo-dermoscopic-features,利用最新的Yolo模型.
实施用于监督学习的JSON注释,用于皮肤病变细分的模型预训练,以及针对特定皮肤镜特征训练五种不同的模型.
在ISIC 2018任务2数据集上进行的实验.

主要成果:

约洛-皮肤镜特征算法实现了高平均性能指标:Dice (0.9758),Jaccard (0.954),精度 (0.9724),回忆 (0.938) 和平均精度 (0.9692).
与现有方法相比,拟议的方法显示出更高的雅卡德相似系数 (0.954),表明与专业注释更好地对齐.
该算法显示了自动图像注释的潜力,解决了数据稀缺问题.

结论:

约洛-皮肤镜特征算法显著提高了自动皮肤镜特征检测的精度.
这种人工智能驱动的方法为皮肤科医生提供了有价值的工具,增强了诊断能力,并可能自动化图像注释.
该方法为分析复杂的皮肤镜像提供了强大的解决方案,提高了诊断准确度.

关键词:

皮肤显微镜的图像功能检测功能检测功能检测一次性物体检测算法

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