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首页> 外文期刊>International journal of wireless and mobile computing >Mobile health framework based on adaptive feature selection of deep convolutional neural network and QoS optimisation for benign-malignant lung nodule classification
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Mobile health framework based on adaptive feature selection of deep convolutional neural network and QoS optimisation for benign-malignant lung nodule classification

机译：基于自适应特征选择的移动健康框架，对深卷积神经网络和QoS优化对良性恶性肺结节分类

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摘要

In order to explore the potential of Deep Learning (DL) methods in mobile health, we propose a novel framework combined enhanced DL methods and Quality of Service (QoS) optimisation for lung nodule classification. First, for classification-based DL methods, the methods of feature extraction and feature selection are widely used as the key steps in the classification of lung nodules. This paper proposes an adaptive feature selection method based on Deep Convolution Neural Network (DCNN). Based on the idea of transfer learning, we firstly use DCNN model pre-trained on ImageNet database to extract the features of multi-channel lung nodules images and then we use adaptive feature selection method extract sparse activation features. The experimental results show that the proposed method does improve the performance of benign and malignant lung nodule classification, which can achieve the classification accuracy of 89.30% and the AUC of 0.94.
机译：为了探讨移动健康中深度学习（DL）方法的潜力，我们提出了一种新颖的框架组合增强的DL方法和肺结节分类的优化（QoS）优化。首先，对于基于分类的DL方法，特征提取和特征选择的方法被广泛用作肺结节分类中的关键步骤。本文提出了一种基于深卷积神经网络（DCNN）的自适应特征选择方法。基于转移学习的想法，我们首先使用DCNN模型在Imagenet数据库上预先培训，提取多通道肺结核图像的特征，然后我们使用自适应特征选择方法提取稀疏激活功能。实验结果表明，该方法确实提高了良性和恶性肺结节分类的性能，可以达到89.30％和0.94的分类精度。

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