Title: Mobile health framework based on adaptive feature selection of deep convolutional neural network and QoS optimisation for benign-malignant lung nodule classification

Authors: Xiao Wang; Huiming Gao; Juanjuan Zhao; Sanhu Wang

Addresses: College of Information Technology and Engineering, Jinzhong University, Shanxi, Jinzhong 030600, China ' College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, Shanxi, China ' College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, Shanxi, China ' Department of Computer Science and Technology, Lvliang University, Lvliang 033000, Shanxi, China

Abstract: 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.

Keywords: mobile health; QoS optimisation; deep convolutional neural network; adaptive feature selection; lung nodule; classification.

DOI: 10.1504/IJWMC.2019.102267

International Journal of Wireless and Mobile Computing, 2019 Vol.17 No.3, pp.307 - 315

Received: 08 May 2019
Accepted: 09 Jul 2019

Published online: 05 Sep 2019 *

Full-text access for editors Access for subscribers Purchase this article Comment on this article