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使用堆叠变压器模型和可解释的人工智能对结肠癌疾病的自动诊断

Lubna Abdelkareim Gabralla¹, Ali Mohamed Hussien², Abdulaziz AlMohimeed³

¹Department of Computer Science and Information Technology, Applied College, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.

Diagnostics (Basel, Switzerland)

|September 28, 2023

概括

一个新的深度学习模型通过堆叠卷积神经网络 (CNN) 模型,准确地预测结肠癌. 这种先进的技术可以改善这种常见疾病的早期检测和治疗结果.

科学领域:

在瘤学瘤学.
计算机科学计算机科学
人工智能的人工智能

背景情况:

结肠癌是全球领先的癌症,在2020年有近200万例.
准确的早期检测对于成功治疗结肠癌至关重要.
深度学习有可能提高医学成像中的诊断准确性.

研究的目的:

提出一种用于结肠癌预测的新型异质堆叠深度学习模型.
用集成深度学习方法提高结肠癌检测的性能.
根据已建立的深度学习架构对拟议的模型进行评估.

主要方法:

开发了一种异质堆叠深度学习模型,集成预训练的卷积神经网络 (CNN) 模型.
使用metalearner来提高堆叠框架内的预测性能.
在LC25000和WCE结肠癌图像数据集 (二进制和多分类) 上评估模型.
与VGG16,InceptionV3,Resnet50和DenseNet121的性能进行比较,使用准确度,回忆,精度和F1分数.

主要成果:

拟议的堆叠深度学习模型在两个数据集上都实现了卓越的性能.
对于LC25000,堆叠模型实现了100%的准确性,回忆,精度和F1得分.
对于WCE,堆叠模型实现了98%的准确性,回忆,精度和F1得分.
堆叠SVM表现出比VGG16,InceptionV3,Resnet50和DenseNet121.1.等单个模型更高的性能.

结论:

异质堆叠深度学习模型显著提高结肠癌预测准确度.
拟议的堆叠方法为结肠癌检测提供了一种强大而高效的方法.
在这种情况下,可以应用可解释AI (XAI) 方法来理解黑子深度学习模型.

关键词:

在美国,CNN是CNN.结肠癌是什么意思结肠癌是什么意思可解释的人工智能 (XAI)堆叠组合合集堆叠组合转移学习学习转移学习

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