优化皮肤癌诊断:用于分类的修改组合卷积神经网络.
A M Vidhyalakshmi1, M Kanchana1
1Department of Computing Technologies, SRM Institute of Science and Technology, Kattankulathur, Tamilnadu, India.
Microscopy research and technique
|January 31, 2025
概括
这项研究引入了一种新的随机猫群优化与整体卷积神经网络 (RCS-ECNN) 进行精确的皮肤癌检测. RCS-ECNN方法显著改善了早期皮肤癌诊断和分类准确度.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 计算生物学 计算生物学
背景情况:
- 皮肤癌是全球主要的健康问题,需要改进诊断方法.
- 传统的皮肤癌检测技术存在可扩展性和超拟合性的局限性.
- 早期检测对于有效的皮肤癌治疗结果至关重要.
研究的目的:
- 提出一种新的随机猫群优化与整体卷积神经网络 (RCS-ECNN) 进行皮肤癌阶段分类.
- 通过深度学习提高皮肤癌检测的准确性和效率.
- 解决现有的皮肤癌诊断方法的局限性.
主要方法:
- 使用了两个深度学习分类器:深度神经网络 (DNN) 和Keras DNN (KDNN).
- 实现了有效的预处理阶段,特征提取和GrabCut算法进行细分.
- 采用随机猫群优化 (CSO) 来优化整体卷积神经网络 (ECNN) 模型.
- 在HAM10000和ISIC数据集上评估了RCS-ECNN方法.
主要成果:
- RCS-ECNN方法实现了高性能指标:准确率为99.56%,回忆率为99.66%,特异性为99.254%,精度为99.18%,F1分数为98.545%.
- 与现有的皮肤癌检测技术相比,其表现优越.
- 拟议的方法有效地分类皮肤癌的不同阶段.
结论:
- RCS-ECNN方法为皮肤癌的检测和分类提供了一个高度准确和高效的方法.
- 这种基于深度学习的策略克服了传统方法的局限性.
- 这些发现表明,在临床环境中改善早期皮肤癌诊断的巨大潜力.
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