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Quantitative 3-D shape features based tumor identification in the fog computing architecture

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Abstract

For the purpose of accurate diagnosis and early treatment for cancers, the classification and identification of different tumors is the key problem of computer-aided diagnosis system. In this paper, an improved semi-supervised tumor identification method is proposed, which takes advantage of the Fuzzy c-means clustering algorithm and offers a pathological degree tree based on ten three-dimentional (3-D) and two dimentional (2-D) tumor features. In addition, a great deal of complicated data processing is distributed in the fog computing architecture. First, we carry out the segmentation of tumors by using FRFCM algorithm, and complete the 3-D modeling. Then, the pathological shape features of 3-D and 2-D tumors are extracted from modeling, for constructing a group of feature vector. Finally, based on the landmark information of labeled samples provided by standard database and experts, we realize an improved semi-supervised FCM clustering to guide the tumor identification. The experiments are conducted by using medical CT scans of 143 patients including 452 tumors. Overall, the best average identification accuracy of \(94.6\%\) has been recorded for this proposed method, the ability of machine learning to recognize the benign, malignant and false-positive tumors is improved effectively under imbalanced data sets.

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Acknowledgements

This work is supported by National Natural Science Foundation (61572286, U1609218 and 61472220), Natural Science Foundation of Shandong Province (2016ZRB 01143), and the Fostering Project of Dominant Discipline an Talent Team of Shandong Province Higher Education. The authors sincerely thank Dr. Guangli Wang for her efforts to carefully label and verify the ground truth of the dataset, also gratefully acknowledge the helpful comments and suggestions of the reviewers, which have improved the presentation significantly.

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Authors and Affiliations

  1. Shandong University of Finance and Economics, Jinan, Shandong, China

    Jun Xu, Hui Liu & Weiran Shao

  2. Shandong Provincial Key Laboratory of Digital Media Technology, Jinan, Shandong, China

    Jun Xu & Hui Liu

  3. Qianfoshan Hospital of Shandong Province, Jinan, Shandong, China

    Kai Deng

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  1. Jun Xu
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  2. Hui Liu
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  3. Weiran Shao
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  4. Kai Deng
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Correspondence to Hui Liu.

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Xu, J., Liu, H., Shao, W. et al. Quantitative 3-D shape features based tumor identification in the fog computing architecture. J Ambient Intell Human Comput 10, 2987–2997 (2019). https://doi.org/10.1007/s12652-018-0695-5

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