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基于混合YOLOv5和ResNet技术的早期黑色素瘤检测

Manar Elshahawy¹, Ahmed Elnemr², Mihai Oproescu³

¹Information Technology Department, Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt.

Diagnostics (Basel, Switzerland)

|September 9, 2023

概括

使用结合YOLOv5和ResNet50.50的新人工智能模型改进了早期黑色素瘤检测. 这种计算机辅助诊断工具提高了皮肤病变的分类准确性,有助于及时治疗.

科学领域:

皮肤病学和人工智能研究
医学成像分析医学成像分析
计算病理学计算病理学

背景情况:

黑色素瘤是一种皮肤癌,由于发病率上升,它对健康构成重大风险.
准确及时识别皮肤病变对于有效治疗黑色素瘤至关重要.
计算机辅助诊断工具正在开发中,以解决分类相似的良性和恶性皮肤病变的挑战.

研究的目的:

开发和评估一种新的计算机辅助诊断模型,用于早期检测黑色素瘤.
提高皮肤病变的皮肤镜像分类的准确性和效率.
为了创建一个更小,更精确的深度学习模型用于黑色素瘤识别.

主要方法:

开发了一个混合深度学习模型,集成YOLOv5 (You Only Look Once) 和ResNet50架构.
功能地图被增强了梯度变化,以快速推断和减少超参数.
通过使用HAM10000数据集,YOLOv5模型被调整为颜色皮肤病变的新类.

主要成果:

拟议的模型实现了高性能指标,包括99.0%的精度,98.6%的回忆率和99.5%的准确性.
实时检测速度达到0.4毫秒/图像 (非最大抑制).
与现有的黑色素瘤检测方法相比,该模型在利用深度特征方面表现出更高的效率.

结论:

结合的YOLOv5和ResNet50模型为黑色素瘤检测提供了一个高度准确和高效的解决方案.
这种由人工智能驱动的方法在帮助临床医生早期和可靠的皮肤癌诊断方面表现有前途.
该模型的提高精度和速度有助于推进皮肤病学中的计算机辅助诊断.

关键词:

在ResNet50网络网络中,ResNet50是皮肤镜图像分析黑色素瘤检测检测方法皮肤癌的分类皮肤癌的分类你只看一次 (YOLO)

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