Title: Automatic diagnosis of pulmonary nodules using a hierarchical extreme learning machine model

Authors: Rui Hao; Zilin Qiang; Yan Qiang; Lei Ge; Juanjuan Zhao

Addresses: College of Information Management, Shanxi University of Finance and Economics, Taiyuan, China ' College of Software, Taiyuan University of Technology, Taiyuan, China ' College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, China ' College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, China ' College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, China

Abstract: To effectively improve the diagnosis of pulmonary nodules, this paper proposes a new automatic diagnosis method for pulmonary nodules based on a new hierarchical extreme learning machine (H-ELM) that can automatically carry out feature extraction, model training and pulmonary nodule detection. In our method, an adaptive histogram equalisation is used first to enhance contrast of the original pulmonary nodule image. The processed images are then input into an extreme learning machine (ELM)-based unsupervised multilayer auto-encoder to obtain more compact and meaningful high-level features of the pulmonary nodule image. Finally, supervised feature classification, which uses these high-level features of the pulmonary nodule as input data, is implemented using the ELM classifier. In the experiments, 2,800 pulmonary nodule images are used to validate the proposed method, and compared with existing pulmonary nodule diagnosis methods, our proposed method is more accurate and less time consuming and effectively avoids the complexity of manual feature extraction.

Keywords: automatic diagnosis; pulmonary nodule; hierarchical extreme learning; adaptive histogram equalisation; sparse autoencoder; feature extraction; feature classification; computer aided diagnosis.

DOI: 10.1504/IJBIC.2018.091748

International Journal of Bio-Inspired Computation, 2018 Vol.11 No.3, pp.192 - 201

Received: 25 Aug 2017
Accepted: 18 Dec 2017
Published online: 14 May 2018 *

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