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Sensors 2019, 19(1), 194;

An Appraisal of Lung Nodules Automatic Classification Algorithms for CT Images

School of Software, Northeastern University, Shenyang 110004, China
Norman Bethune Health Science Center of Jilin University, No. 2699 Qianjin Street, Changchun 130012, China
School of Computer Science and Engineering, Northeastern University, Shenyang 110004, China
Author to whom correspondence should be addressed.
Received: 16 November 2018 / Revised: 28 December 2018 / Accepted: 31 December 2018 / Published: 7 January 2019
(This article belongs to the Special Issue Biomedical Infrared Imaging: From Sensors to Applications)
PDF [2873 KB, uploaded 9 January 2019]


Lung cancer is one of the most deadly diseases around the world representing about 26% of all cancers in 2017. The five-year cure rate is only 18% despite great progress in recent diagnosis and treatment. Before diagnosis, lung nodule classification is a key step, especially since automatic classification can help clinicians by providing a valuable opinion. Modern computer vision and machine learning technologies allow very fast and reliable CT image classification. This research area has become very hot for its high efficiency and labor saving. The paper aims to draw a systematic review of the state of the art of automatic classification of lung nodules. This research paper covers published works selected from the Web of Science, IEEEXplore, and DBLP databases up to June 2018. Each paper is critically reviewed based on objective, methodology, research dataset, and performance evaluation. Mainstream algorithms are conveyed and generic structures are summarized. Our work reveals that lung nodule classification based on deep learning becomes dominant for its excellent performance. It is concluded that the consistency of the research objective and integration of data deserves more attention. Moreover, collaborative works among developers, clinicians, and other parties should be strengthened. View Full-Text
Keywords: lung nodule classification; computer tomography; medical image analysis; pattern recognition lung nodule classification; computer tomography; medical image analysis; pattern recognition

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Wang, X.; Mao, K.; Wang, L.; Yang, P.; Lu, D.; He, P. An Appraisal of Lung Nodules Automatic Classification Algorithms for CT Images. Sensors 2019, 19, 194.

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