Computer Science ›› 2017, Vol. 44 ›› Issue (8): 312-317.doi: 10.11896/j.issn.1002-137X.2017.08.054

Special Issue: Medical Imaging

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Pulmonary Nodule Diagnosis Using Dual-modal Denoising Autoencoder Based on Extreme Learning Machine

ZHAO Xin, QIANG Yan and GE Lei   

  • Online:2018-11-13 Published:2018-11-13

Abstract: The existing deep learning framework used in diagnosing lung cancer still mainly focuses on lung Computed Tomography(CT) images,but it cannot obtain more higher diagnostic rate,when using only one images in the process of daily diagnosis.Therefore,in this paper,a new pulmonary nodule diagnosis method using dual-modal combined with CT and Positron Emission Tomography(PET) deep denoising autoencoder based on extreme learning machine (SDAE-ELM) was proposed to improve the diagnostic performance effectively.First of all,the method gets discriminative features information separate from the input data CT and PET.Secondly,it inputs CT and PET about candidate lung respectively in whole network.Thirdly,it extracts the high level discriminative features of nodules by alternating stack denoising autoencoder layers.Finally,it makes the fusion strategy of multi-feature fusion as the output of the whole framework.The experiment results show that classification accuracy of the proposed method can reach 92.81%,sensitivities up to 91.75% and specificity up to 1.58%.Meanwhile,the method achieves better discriminative results and is highly suited to be used for pulmonary nodule diagnosis.

Key words: Denoising autoencoder,Dual-modal,Deep learning,Extreme learning machine,Pulmonary nodule diagnosis

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