使用合成深度学习模型与元启发优化算法进行乳腺癌分类
Selvakumar Thirumalaisamy1, Kamaleshwar Thangavilou2, Hariharan Rajadurai3
1Department of Artificial intelligence & Data Science, Dr. Mahalingam College of Engineering and Technology, Pollachi 642003, India.
Diagnostics (Basel, Switzerland)
|September 28, 2023
概括
这项研究介绍了EACO-ResNet101,这是一种用于乳腺癌检测的新型深度学习模型. 它显著提高了从乳房影像分类乳腺癌的准确性,帮助放射科医生在早期异常识别.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 在瘤学瘤学.
背景情况:
- 乳腺癌仍然是女性死亡的主要原因,这强调了早期和准确检测的必要性.
- 传统的乳腺癌检测和分类方法存在局限性.
- 卷积神经网络 (CNN) 在增强用于瘤识别的医疗图像分析方面表现有前途.
研究的目的:
- 开发用于乳腺癌检测的综合分类技术,使用合成的CNN和增强的优化算法.
- 帮助放射科医生快速准确地识别乳腺癌异常.
- 提高乳腺癌分类在乳腺癌数据集中的准确性,敏感性和特异性.
主要方法:
- 开发了一种新的增强殖民地优化 (EACO) 算法,修改了基于对立的学习 (OBL),以优化CNN的超参数.
- 该EACO算法与剩余网络-101 (ResNet101) CNN架构集成,创建了EACO-ResNet101模型.
- 拟议的模型是根据MIAS和CBIS-DDSM的乳腺学数据集进行评估的.
主要成果:
- 在CBIS-DDSM数据集上,EACO-ResNet101模型实现了高性能,准确度为98.63%,灵敏度为98.76%,特异性为98.89%.
- 在MIAS数据集上,该模型显示了99.15%的准确性,97.86%的灵敏度和98.88%的特异性.
- 拟议的模型在乳腺癌分类方面显著优于传统方法.
结论:
- 开发的EACO-ResNet101模型为乳腺癌分类提供了一种优越的方法.
- 这种由人工智能驱动的技术有可能提高诊断准确度,并支持放射科医生的临床决策.
- 这些发现强调了将先进的优化算法与医学图像分析的深度学习相结合的有效性.
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