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一种新的集体学习方法,用于分组最先进的YOLOV10和YOLOV11模型,用于CT和超声波图像中检测结石

Ali Mahmoud Mayya¹, Nizar Faisal Alkayem²

¹Computer and Automatic Control Engineering Department, Faculty of Mechanical and Electrical Engineering, Latakia University (Formerly Called Tishreen University), Latakia, 2230, Syria.

Journal of imaging informatics in medicine

|April 15, 2025

概括

这项研究引入了一种新的深度学习组合模型,用于改善医疗图像中的结石检测. 新的框架提高了检测准确性,超过了单个模型和当前方法.

科学领域:

医疗成像医学成像
人工智能的人工智能
腎臟病學 (nephrology) 是一種醫學專業.

背景情况:

检测石对于保持脏健康至关重要,但在医学成像中仍然是一个具有挑战性的任务.
准确和精确地定位结石对于有效的治疗计划至关重要.
关于结石检测方法的学术文献有限.

研究的目的:

开发一个先进的深度学习框架,以准确检测结石.
为了提高医疗图像中结石识别的精度和回忆力.
创建一个组合模型,将YOLOV10和YOLOV11结合起来,以提高性能.

主要方法:

使用了两个成像模式:计算机断层扫描 (CT) 和超声波.
开发了一个新的整体框架,集成YOLOV10和YOLOV11深度学习模型.
对集体模型的表现与单个模型和最先进的方法进行了评估.

主要成果:

与CT图像上的最佳单独模型相比,整体模型的精度提高了5.4%,回忆率提高了2.4%,F1得分提高了1.3%.
基于超声波的数据集显示F1得分有1%的改善,Map50得分有1.34%的增加.
拟议的深度学习整体框架在单个模型和现有方法论上表现出卓越的性能.

结论:

新型组合框架显著提高了结石检测的准确性,并减少了错误.
将YOLOV10和YOLOV11结合在一个整体方法中,为结石检测提供了卓越的性能.
这种深度学习方法显示出改善科诊断能力的前景.

关键词:

深度学习是一种深度学习.合唱团组合在一起.检测结石检测结石的检测对象检测检测对象检测对象检测在YOLOV10上,你会看到YOLOV10.这就是YOLOV11的原因.

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