Open Access

ARTICLE

Multi-View Auxiliary Diagnosis Algorithm for Lung Nodules

Shi Qiu¹, Bin Li^2,*, Tao Zhou³, Feng Li⁴, Ting Liang⁵

1 Key Laboratory of Spectral Imaging Technology CAS, Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an, 710119, P.R. China
2 School of Information Science and Technology, Northwest University, Xi’an, 710127, P.R. China
3 School of Computer Science and Engineering, North Minzu University, Yinchuan, 750021, P.R. China
4 Institute of Education, University College London, London, The United Kingdom
5 Department of Radiology, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, 10061, P.R. China

* Corresponding Author: Bin Li. Email:

Computers, Materials & Continua 2022, 72(3), 4897-4910. https://doi.org/10.32604/cmc.2022.026855

Received 05 January 2022; Accepted 11 March 2022; Issue published 21 April 2022

Abstract

Lung is an important organ of human body. More and more people are suffering from lung diseases due to air pollution. These diseases are usually highly infectious. Such as lung tuberculosis, novel coronavirus COVID-19, etc. Lung nodule is a kind of high-density globular lesion in the lung. Physicians need to spend a lot of time and energy to observe the computed tomography image sequences to make a diagnosis, which is inefficient. For this reason, the use of computer-assisted diagnosis of lung nodules has become the current main trend. In the process of computer-aided diagnosis, how to reduce the false positive rate while ensuring a low missed detection rate is a difficulty and focus of current research. To solve this problem, we propose a three-dimensional optimization model to achieve the extraction of suspected regions, improve the traditional deep belief network, and to modify the dispersion matrix between classes. We construct a multi-view model, fuse local three-dimensional information into two-dimensional images, and thereby to reduce the complexity of the algorithm. And alleviate the problem of unbalanced training caused by only a small number of positive samples. Experiments show that the false positive rate of the algorithm proposed in this paper is as low as 12%, which is in line with clinical application standards.

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Keywords

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