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物联网辅助的智能皮肤癌检测使用深度学习模型的元启发学

Marwa Obayya¹, Munya A Arasi², Nabil Sharaf Almalki³

¹Department of Biomedical Engineering, College of Engineering, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.

|October 28, 2023

概括

本研究介绍了一种最佳的深度学习模型,用于物联网 (IoT) 辅助的皮肤癌检测. 新的ODL-SCDC方法提高了皮肤病变的早期诊断准确度.

科学领域:

医疗信息学医疗信息学
人工智能的人工智能
皮肤病学皮肤病学

背景情况:

皮肤癌的诊断是具有挑战性的,因为病变的变化和成像工件.
深度学习 (DL) 和物联网 (IoT) 提供了改善早期检测的潜力.
现有的方法难以准确分析各种皮肤病变特征.

研究的目的:

在物联网环境中提出基于深度学习的皮肤癌检测和分类 (ODL-SCDC) 最佳方法.
通过先进的DL技术,增强早期皮肤癌分析和监测.
利用元启发式算法优化DL模型在皮肤癌分类中的性能.

主要方法:

该 ODL-SCDC 方法利用算术优化算法 (AOA) 与EfficientNet进行特征提取.
一个堆叠的DenoisingAutoencoder (SDAE) 模型被用于皮肤癌的检测.
龙算法 (DFA) 优化了SDAE模型的超参数,以改善分类.
验证是在ISIC皮肤病变数据库上进行的.

主要成果:

在ISIC数据集上,ODL-SCDC方法实现了高性能指标.
达到97.74%的最大灵敏度,99.71%的特异性和99.55%的准确性.
在皮肤癌分类中,与现有模型相比,表现优越.

结论:

拟议的ODL-SCDC模型在物联网环境中显著提高了皮肤癌检测和分类准确度.
这种人工智能驱动的方法可以帮助医疗专业人员,包括皮肤科医生,在诊断过程中.
DL和物联网的整合为推进皮肤病护理和早期癌症检测提供了一个有希望的途径.

关键词:

物联网的物联网,就是物联网.深度学习是一种深度学习.皮肤显微镜的图像这是一种超听证学 (metaheuristics).皮肤癌的诊断皮肤癌的诊断

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