Detection and Classification of Lung Nodule in Diagnostic CT: A TsDN method based on Improved 3D-Faster R-CNN and Multi-Scale Multi-Crop Convolutional Neural Network
AUTHORS
Muhammad Bilal Zia,Graduate school of Information and Computer Science, Taiyuan University of Technology, Taiyuan, China
Juan Juan Zhao,Graduate school of Information and Computer Science, Taiyuan University of Technology, Taiyuan, China
Xiao Ning,Graduate school of Information and Computer Science, Taiyuan University of Technology, Taiyuan, China
ABSTRACT
Lung nodule classification has been one of the major problem relevant to Computer-Aided Diagnosis (CAD) system. Lung cancer for both men and women has been one of the leading causes of cancer related death. Deep learning models have produced promising performance in recent years, outperforming traditional methods in different fields. Nowadays, scientists have attempted numerous deep learning approaches to enhance the efficiency of CAD systems via Computed Tomography (CT) in lung cancer screening. In this paper, we presented a completely automatic lung CT system for cancer diagnosis named Two-step Deep Network (TsDN) and it contains two parts detection of nodule and classification. First, Improved 3D-Faster R-CNN with U-net like encoder and decoder is used for detection of nodule and then Multi-scale Multi-crop Convolutional Neural Network (MsMc-CNN) is proposed for the pulmonary nodule classification. The multi scale approach uses filters of various sizes to extract nodule features more efficiently from the local regions, and then multi crop pooling technique involves in extracting the important nodule information that cultivates various regions from convolutional feature map and then add numerous times for the maximum pooling. The proposed TsDN is trained and evaluated on LIDC-IDRI public dataset and achieved a sensitivity of 0.885 and specificity of 0.922 with AUC of 0.946.
KEYWORDS
Detection, Classification, 3D-Faster R-CNN, Multi-scale Multi-crop CNN
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CITATION
COPYRIGHT
© 2020 Muhammad Bilal Zia et al. Published by Global Vision Press. This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CCBY4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.