Pulmonary Nodule Classification Using a Multiview Residual Selective Kernel Network

Herng-Hua Chang; Cheng-Zhe Wu; Audrey Haihong Gallogly

doi:10.1007/s10278-023-00928-4

Pulmonary Nodule Classification Using a Multiview Residual Selective Kernel Network

J Imaging Inform Med. 2024 Feb;37(1):347-362. doi: 10.1007/s10278-023-00928-4. Epub 2024 Jan 11.

Authors

Herng-Hua Chang¹, Cheng-Zhe Wu², Audrey Haihong Gallogly³

Affiliations

¹ Computational Biomedical Engineering Laboratory (CBEL), Department of Engineering Science and Ocean Engineering, National Taiwan University, 1 Sec. 4 Roosevelt Road, Daan, Taipei, 10617, Taiwan. herbertchang@ntu.edu.tw.
² Computational Biomedical Engineering Laboratory (CBEL), Department of Engineering Science and Ocean Engineering, National Taiwan University, 1 Sec. 4 Roosevelt Road, Daan, Taipei, 10617, Taiwan.
³ Department of Radiation Oncology, Keck Medical School, University of Southern California, Los Angeles, CA, USA.

Abstract

Lung cancer is one of the leading causes of death worldwide and early detection is crucial to reduce the mortality. A reliable computer-aided diagnosis (CAD) system can help facilitate early detection of malignant nodules. Although existing methods provide adequate classification accuracy, there is still room for further improvement. This study is dedicated to investigating a new CAD scheme for predicting the malignant likelihood of lung nodules in computed tomography (CT) images in light of a deep learning strategy. Conceived from the residual learning and selective kernel, we investigated an efficient residual selective kernel (RSK) block to handle the diversity of lung nodules with various shapes and obscure structures. Founded on this RSK block, we established a multiview RSK network (MRSKNet), to which three anatomical planes in the axial, coronal, and sagittal directions were fed. To reinforce the classification efficiency, seven handcrafted texture features with a filter-like computation strategy were explored, among which the homogeneity (HOM) feature maps are combined with the corresponding intensity CT images for concatenation input, leading to an improved network architecture. Evaluated on the public benchmark Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) challenge database with ten-fold cross validation of binary classification, our experimental results indicated high area under receiver operating characteristic (AUC) and accuracy scores. A better compromise between recall and specificity was struck using the suggested concatenation strategy comparing to many state-of-the-art approaches. The proposed pulmonary nodule classification framework exhibited great efficacy and achieved a higher AUC of 0.9711. The association of handcrafted texture features with deep learning models is promising in advancing the classification performance. The developed pulmonary nodule CAD network architecture is of potential in facilitating the diagnosis of lung cancer for further image processing applications.

Keywords: CT; Image classification; Lung nodule; Residual learning; Selective kernel.

Abstract

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