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深度学习模型用于以基尼为基础的特征选择和以线性生产为灵感的特征融合等级癌症

Shreyan Kundu¹, Souradeep Mukhopadhyay², Rahul Talukdar¹

¹Department of Computer Science & Engineering, Institute of Engineering & Management, Kolkata, India, 700091.

Scientific reports

|July 2, 2025

概括

这项研究引入了一个新的框架,使用基于注意力的卷积神经网络 (CNN) 和经济理论来改善细胞癌 (RCC) 和肝细胞癌 (HCC) 的分级. 该方法在从组织病理学图像中对这些常见癌症进行分类时,达到很高的准确性.

科学领域:

在瘤学瘤学.
医疗成像医学成像
计算病理学计算病理学

背景情况:

对细胞癌 (RCC) 和肝细胞癌 (HCC) 的准确分类对于有效的癌症治疗策略至关重要.
传统的深度学习模型在RCC和HCC中准确分类复杂的组织病理模式时面临着挑战.
现有的方法往往缺乏可靠癌症分类所需的精度.

研究的目的:

开发一个先进的特征选择和融合框架,以提高肝脏和脏癌症的分级.
通过使用深度学习,提高细胞癌 (RCC) 和肝细胞癌 (HCC) 的分类准确性.
整合注意力机制和经济理论启发的方法,以进行强大的癌症分级.

主要方法:

在MobileNetV2,DenseNet121和InceptionV3卷积神经网络 (CNN) 架构中整合注意力机制.
基于基尼的特征选择方法的实施,以从基因病理图像中识别歧视性特征.
使用经济理论启发的线性生产函数来改进预测,提取的特征的最佳融合.

主要成果:

拟议的框架实现了高分类准确度:93.04%的细胞癌 (RCC) 和98.24%的肝细胞癌 (HCC).
与癌症分级的现有最先进模型相比,表现出卓越的性能.
验证了新型特征选择和融合方法的稳定性和有效性.

结论:

开发的基于注意力的CNN框架与经济理论启发的特征选择和融合显著提高了RCC和HCC分级的准确性.
这种方法为改善肝癌和癌的诊断精度提供了一个有希望的工具.
公开可用的代码促进了计算机病理学的进一步研究和应用.

关键词:

自动化分级系统自动化分级系统癌症的诊断癌症的诊断细胞癌细胞癌.深度学习是一种深度学习.功能提取功能提取组织病理学图像分析分析.图像的分类图像的分类.机器学习机器学习医学成像医学成像

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