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用深度特征提取和多层感知子来分类骨肉瘤的新型混合方法

Md Tarek Aziz¹, S M Hasan Mahmud^1,2, Md Fazla Elahe^1,3

¹Centre for Advanced Machine Learning and Applications (CAMLAs), Bashundhara R/A, Dhaka 1229, Bangladesh.

Diagnostics (Basel, Switzerland)

|June 28, 2023

概括

这项研究引入了一种混合人工智能模型,用于从整个幻灯片图像中分类骨髓瘤骨癌亚型. 该模型实现了高精度,帮助病理学家诊断年轻成年人这种常见的癌症.

科学领域:

计算病理学计算病理学
人工智能在瘤学中的应用
数字组织病理学数字组织病理学

背景情况:

骨肉瘤是最常见的骨癌,主要影响青少年和年轻人.
对H&E染色组织的组织病理学分析由于图像复杂性,噪音和类相似性而存在挑战.
精确地分类骨髓瘤亚型 (非瘤,亡,可活瘤) 对于有效的治疗计划至关重要.

研究的目的:

开发和评估一种混合深度学习框架,用于增强骨髓瘤瘤分类.
为了提高诊断准确性和从整个幻灯片图像 (WSIs) 来分类骨髓瘤亚型的效率.
整合一个强大的特征选择机制,以实现最佳模型性能.

主要方法:

开发了一个混合框架,将预先训练的卷积神经网络 (CNN) 模型与多层感知器 (MLP) 分类器结合起来.
使用五个CNN架构作为预处理的WSIs上的特征提取器,采用转移学习.
使用决策树估计器的递归特征消除 (RFE) 用于特征选择,然后用五倍交叉验证进行MLP分类.

主要成果:

拟议的混合模型实现了高精度:95.2%的多类分类和99.4%的二进制分类.
功能选择分析确定了最佳标准,平衡执行时间和分类准确性.
该模型在骨髓瘤分类中与现有方法相比,表现优越.

结论:

开发的混合人工智能模型显著提高了从数字组织病理学幻灯片的骨髓瘤瘤分类的准确性.
这种方法提供了一种有价值的工具来帮助临床医生诊断骨髓瘤,可能改善患者的治疗结果.
该模型集成到Web应用程序中,可实现实时预测,促进临床应用.

关键词:

在MLP中,MLP是MLP.卷积神经网络是一种卷积神经网络.功能提取特性提取功能选择功能选择机器学习是机器学习.骨质肉瘤是骨质肉瘤的一种疾病.转移学习转移学习

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