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有效预测人类皮肤癌使用堆叠基于集团深度学习算法

David Neels Ponkumar Devadhas¹, Hephzi Punithavathi Isaac Sugirtharaj², Mary Harin Fernandez³

¹Department of Electronics and Communication Engineering, Vel Tech Rangarajan Dr.Sagunthala R & D Institute of Science and Technology, Chennai, India.

Network (Bristol, England)

|May 28, 2024

概括

这项研究引入了一种先进的深度学习模型,用于自动化皮肤癌诊断. 整体模型在从病变图像中识别皮肤癌时达到97%的准确性.

科学领域:

医疗成像医学成像
人工智能的人工智能
皮肤病学皮肤病学

背景情况:

根据病变图像自动诊断皮肤癌是具有挑战性的,因为颜色,照明,尺寸和形状的变化.
现有的方法难以处理皮肤病变的复杂视觉特征.

研究的目的:

开发一个准确和强大的皮肤癌诊断自动化系统,使用一组深度学习技术.
改进对具有挑战性的皮肤病变图像的解释,以更可靠地检测癌症.

主要方法:

一个新的集体深度学习模型,结合了卷积神经网络 (CNN),深度神经网络 (DNN) 和长短期记忆 (LSTM) 网络.
图像预处理包括调整尺寸和异型扩散; 模糊C-Means聚类进行细分; 局部二进制模式 (LBP) 进行特征提取.
用LSTM作为元分类器的基于堆叠的组合方法被用于最终分类.

主要成果:

拟议的集体深度学习模型实现了97%的高诊断精度.
整合CNN,DNN和LSTM有效处理图像变化和复杂的病变特征.
模糊-C-Means细分和LBP特征提取有助于提高分类性能.

结论:

开发的集体深度学习模型显示了准确和自动化皮肤癌诊断的巨大潜力.
这种方法为克服解读皮肤病变医学图像所面临的挑战提供了一个有希望的解决方案.
这种高精度表明,它可以在临床上应用,帮助皮肤科医生早期发现癌症.

关键词:

卷积神经网络 (CNN) 是一种神经网络.深度神经网络 (DNN) 是一种神经网络.模糊-C-意味着集群.当地的二进制模式 (LBP)组合学习组合学习

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