一种新的方法用于乳腺癌检测,使用Nesterov加速的adam优化器与注意力机制
Abeer Saber1, Tamer Emara2, Samar Elbedwehy3
1Information Technology Department, Faculty of Computers and Artificial Intelligence, Damietta University, Damietta, 34517, Egypt. abeer_saber@du.edu.eg.
Scientific reports
|July 27, 2025
概括
这项研究引入了一种先进的深度学习模型,用于在超声波图像中自动检测乳腺瘤. 该模型在识别瘤类型方面实现了高精度,提高了诊断效率.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 计算机视觉 计算机视觉
背景情况:
- 传统的乳腺瘤检测依赖于手动图像分析,这是耗时的,需要专门的专业知识.
- 机器学习 (ML) 和计算机视觉,特别是卷积神经网络 (CNN) 的进步,使自动化特征学习能够改善疾病检测.
研究的目的:
- 开发和评估一个深度神经网络模型,用于在超声波图像中准确识别乳腺瘤.
- 为了提高MobileNet-V2架构的性能,使用卷积块注意力机制和Nadam优化器来提高分类准确性.
主要方法:
- 使用MobileNet-V2架构开发了一个深度神经网络模型.
- 集成了一个卷积块注意力机制,以突出超声波图像中受疾病影响的区域.
- 使用Nesterov加速的自适应时刻估计 (Nadam) 优化器来改进提取的特征并增强模型的融合.
主要成果:
- 拟议的模型在80-20数据分割的BUSI数据集上实现了99.1%的准确性,99.7%的灵敏性和99.5%的特异性.
- 在10倍的交叉验证下,该模型显示了98.7%的准确性,99.1%的灵敏度和0.99.9的AUC.
- 注意模块和Nadam优化器显著提高了MobileNet-V2模型在乳腺瘤分类中的性能.
结论:
- 开发的深度学习模型,增强了注意力机制和Nadam优化,显示了在超声波成像中准确和高效的自动乳腺瘤检测的重大前景.
- 这种方法为克服传统手工方法的局限性提供了潜在的解决方案,为更广泛和实际的临床应用铺平了道路.
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