Image classification of lung nodules by requiring the integration of Attention Mechanism into ResNet model

Khai Dinh Lai, T. Le, T. T. Nguyen
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Abstract

In this research, in order to accurately diagnose lung nodules using the LUNA16 dataset, a deep learning model, ResNetl01, is analyzed and chosen. The paper includes: (1) demonstrating the efficiency of the ResNetl01 network on the LUNA16; (2) analyzing the benefits and drawbacks of Attention modules before selecting the best Attention module to integrate into the ResNetl01 model in the classification of lung nodules in CT scans challenge; (3) comparing the efficacy of the proposed model to prior outcomes to demonstrate the model’s feasibility.
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将注意机制整合到ResNet模型中的肺结节图像分类
为了利用LUNA16数据集准确诊断肺结节,本研究对深度学习模型resnet01进行了分析和选择。本文包括:(1)在LUNA16上演示ResNetl01网络的效率;(2)分析各Attention模块的优缺点,选择最佳的Attention模块整合到resnet01模型中,在CT扫描中对肺结节进行分类挑战;(3)将提出的模型的有效性与先前的结果进行比较,以证明模型的可行性。
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