整合混合转移学习与注意力增强的深度学习模型,以改善乳腺癌诊断
Sudha Prathyusha Jakkaladiki1, Filip Maly1
1Faculty of Informatics and Management, University of Hradec Králové, Hradec Kralove, Hradec Kralove, Czech Republic.
PeerJ. Computer science
|March 4, 2024
概括
这项研究引入了一个深度学习模型,用于准确预测乳腺癌,达到高达100%的准确性. 先进的人工智能系统有助于早期发现和诊断癌症.
科学领域:
- 在瘤学瘤学.
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 乳腺癌对全球健康构成重大挑战,需要先进的诊断工具.
- 深度学习 (AI) 为分析复杂的医疗图像数据提供了强大的功能.
- 计算机辅助诊断对于早期发现疾病和改善患者的治疗结果至关重要.
研究的目的:
- 开发和评估一个深度学习模型,用于准确的乳腺癌识别和瘤分类.
- 通过使用基准数据集对现有最先进的方法进行模型性能评估.
主要方法:
- 使用BreakHis和威斯康星乳腺癌 (WBC) 数据集训练一个深度神经网络.
- 利用预处理的医学成像数据进行模型培训,验证和测试.
- 通过包括精度,F1分数,回忆和精度在内的指标来评估模型性能.
主要成果:
- 深度学习模型在BreakHis数据集上达到100%的准确性,在WBC数据集上达到99%.
- 记录了特殊的性能指标,包括99.8%的回忆率和99.06%的F1分数在BreakHis.上.
- 与现有的乳腺癌诊断方法相比,拟议的模型表现出优越的性能.
结论:
- 深度学习模型显示了准确可靠的乳腺癌诊断的巨大潜力.
- 开发的模型为提高乳腺瘤的早期检测和分类提供了一个有前途的工具.
- 进一步将人工智能纳入医疗诊断可以改善患者护理和治疗结果.
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