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A deep CNN based transfer learning method for false positive reduction

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Abstract

A low false positive (FP) rate is of great importance for the use of a Computer Aided Detection (CAD) system to detect pulmonary nodules in thoracic Computed Tomography (CT). However, due to the variations of nodules in appear and size, it is still a very challenging task to obtain a low FP rate. In this paper, we propose a deep Convolutional Neural Network (CNN) based transfer learning method for FP reduction in pulmonary nodule detection on CT slices. We utilized one of the state-of-the-art CNN models, VGG-16 [4], as a feature extractor to obtain nodule features, and used a support vector machine (SVM) for nodule classification. Firstly we transferred all the layers from a pre-trained VGG-16 model in ImageNet to our target networks. Then, we tuned the last fully connected layers to adjust the computer-vision-task-trained CNN model to pulmonary nodule classification task. The initial CNN filter weights were then optimized using the training data, i.e., the pulmonary nodule patch images and corresponding labels through back-propagation so that they better reflected the modalities in the pulmonary nodule image dataset. Finally, features learned in the fine-tuned CNN were used to train a SVM classifier. The output of the trained SVM was used for final classification. Experimental results show that the overall sensitivity of the proposed method was 87.2% with 0.39 FPs per scan, which is higher than 85.4% with 4 FPs per scan obtained by other state of art method.

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Acknowledgments

This work was supported in part by a grant from the National Natural Science Foundation of China (No. 61202198, No.61401355), a grant from the China Scholarship Council (No.201608610048) and the Nature Science Foundation of Science Department of PeiLin count at Xi’an(GX1619), the Key Laboratory Foundation of Shaanxi Education Department, China (No.14JS072). The authors gratefully acknowledge the helpful comments and suggestions of the reviewers.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Xi’an University of Technology, Xi’an, 710048, China

    Zhenghao Shi, Huan Hao, Minghua Zhao, Yaning Feng & Yinghui Wang

  2. School of Information Science and Technology, Aichi Prefectural University, Nagakute, Aichi, 4801198, Japan

    Lifeng He

  3. Department of Electrical and Computer Engineering, Illinois Institute of Technology, Chicago, IL, 60616, USA

    Kenji Suzuki

  4. World Research Hub Initiative, Institute of Innovative Research, Tokyo Institute of Technology, Yokohama, Kanagawa, 226-8503, Japan

    Kenji Suzuki

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  1. Zhenghao Shi
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Correspondence to Zhenghao Shi, Lifeng He or Kenji Suzuki.

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Shi, Z., Hao, H., Zhao, M. et al. A deep CNN based transfer learning method for false positive reduction. Multimed Tools Appl 78, 1017–1033 (2019). https://doi.org/10.1007/s11042-018-6082-6

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  • DOI: https://doi.org/10.1007/s11042-018-6082-6

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