通过优化分布式囊神经网络检测表皮病变
Vineet Kumar Dubey1, Vandana Dixit Kaushik1
1Department of Computer Science & Engineering, Harcourt Butler Technical University, Kanpur, 208002, India.
Computers in biology and medicine
|December 10, 2023
概括
一个新的基于黄金优化的分布式囊神经网络 (GHO-DCaNN) 提高了皮肤癌检测的准确性. 这种方法提高了皮肤病变的早期诊断,包括黑色素瘤,以获得更好的患者结果.
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:
- 包括黑色素瘤在内的皮肤癌对全球健康构成重大风险,早期诊断对生存至关重要.
- 由于症状的复杂性和可变性,准确诊断皮肤病变仍然具有挑战性.
- 现有的诊断方法需要改进,以提高准确性和效率.
研究的目的:
- 引入一种新的计算方法,用于增强皮肤癌检测.
- 提高诊断皮肤病变,特别是黑色素瘤的准确性和可靠性.
- 开发一个优化的深度学习模型,用于精确地识别表皮损伤.
主要方法:
- 开发了一个基于黄金优化的分布式囊神经网络 (GHO-DCaNN).
- 使用下水道飞优化 (SSFO) 进行了基于集群的优化细分方法.
- GHO-DCaNN模型是使用灵感来自金和火行为的混合GHO优化器进行训练的.
主要成果:
- 在GHO-DCaNN实现了皮肤病变检测的高性能指标.
- 具体性,灵敏度和准确率达到97.53%,99.05%和98.83%,90%的训练数据.
- 通过十倍的交叉验证,性能提高到97.83%的特异性,99.50%的灵敏性和99.06%的准确性.
结论:
- 拟议的GHO-DCaNN显示了准确和高效的皮肤癌诊断的巨大潜力.
- 集成先进的优化算法增强了病变细分和特征提取.
- 这种计算方法在皮肤医学医学成像领域提供了有前途的进步.
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