一种自动方法来检测皮肤癌,利用主动红外热学
Ricardo F Soto1, Sebastián E Godoy1
1Department of Electrical Engineering, Universidad de Concepción, Edmundo Larenas 219, Concepción, 4030000, Biobío, Chile.
Heliyon
|December 17, 2024
概括
这项研究引入了新的功能和使用U-Net进行自动化病变选择,以改善皮肤癌检测. 该系统实现了高精度,与专家方法相美,为恶性瘤识别提供了标准化和高效的解决方案.
科学领域:
- 皮肤病学 皮肤病学
- 医疗成像医学成像
- 机器学习 机器学习
背景情况:
- 皮肤癌是一个重要的全球健康问题,发病率不断增加.
- 目前用于识别恶性的方法依赖于视觉检查,皮肤镜检查和患者病史.
- 活跃温度学为区分恶性和良性皮肤病变提供了一种有前途的方法.
研究的目的:
- 引入新的空间和时间特征,以提高皮肤病变恶性瘤分类的准确性.
- 开发一种用于病变细分和恶性瘤检测的自动化系统,克服手动选择的局限性.
- 将这些功能集成到支向量机器分类器中,以提高诊断性能.
主要方法:
- 从皮肤病变中提取新的时空特征.
- 实现U-Net卷积神经网络用于自动化病变细分.
- 使用带有增强功能集的支持向量机器对恶性瘤进行分类.
- 使用雅卡德指数对专家皮肤科医生细分的自动化细分的验证.
主要成果:
- 与半自动算法相比,自动化系统在雅卡德指数中实现了17%的改进.
- 在85%的灵敏度下,U-Net细分实现了68.80%的特异性,相当于非专家细分 (69.63%).
- 拟议的自动检测系统在低质量的临床图像上表现出比ResNet152V2高3%的精度.
结论:
- 开发的系统提供了一种标准化和高效的皮肤癌检测方法,与高精度技术相比.
- 使用U-Net的自动化损伤细分是一种可行的方法,产生与专家细分相竞争的结果.
- 新的空间和时间特征提高了分类准确性,并且可以适应各种机器学习模型.
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