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Classification of benign and malignant parotid tumors based on CT images combined with stack generalization model

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Abstract

Parotid tumors are among the most prevalent tumors in otolaryngology, and malignant parotid tumors are one of the main causes of facial paralysis in patients. Currently, the main diagnostic modality for parotid tumors is computed tomography, which relies mainly on the subjective judgment of clinicians and leads to practical problems such as high workloads. Therefore, to assist physicians in solving the preoperative classification problem, a stacked generalization model is proposed for the automated classification of parotid tumor images. A ResNet50 pretrained model is used for feature extraction. The first layer of the adopted stacked generalization model consists of multiple weak learners, and the results of the weak learners are integrated as input data in a meta-classifier in the second layer. The output results of the meta-classifier are the final classification results. The classification accuracy of the stacked generalization model reaches 91%. Comparing the classification results under different classifiers, the stacked generalization model used in this study can identify benign and malignant tumors in the parotid gland effectively, thus relieving physicians of tedious work pressure.

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Funding

This work was supported by Tianshan Talent-Young Science and Technology Talent Project （NO.2022TSYCCX0060), Xinjiang Uygur Autonomous Region Youth Science Foundation Project (2022D01C695).

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Author notes

  1. NaZiLa HaLiMaiMaiTi and Yue Hong contributed equally to this work and should be considered co-first authors.

Authors and Affiliations

  1. College of Software, Xinjiang University, Urumqi, 830046, China

    NaZiLa HaLiMaiMaiTi, Xiaoyi Lv & Cheng Chen

  2. People’s Hospital of Xinjiang Uygur Autonomous Region, UrumqiXinjiang, 830001, China

    Yue Hong

  3. College of Information Science and Engineering, Xinjiang University, Urumqi, 830046, China

    Min Li & Chen Chen

  4. The Affiliated Cancer Hospital of Xinjiang Medical University, Urumqi, 830011, China

    Hongtao Li

  5. The First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830000, China

    Yunling Wang

  6. Key Laboratory of Software Engineering Technology, Xinjiang University, Urumqi, 830046, China

    Xiaoyi Lv

  7. Key Laboratory of Signal Detection and Processing, Xinjiang University, Urumqi, 830046, China

    Xiaoyi Lv

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  1. NaZiLa HaLiMaiMaiTi
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  3. Min Li
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Correspondence to Xiaoyi Lv or Cheng Chen.

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HaLiMaiMaiTi, N., Hong, Y., Li, M. et al. Classification of benign and malignant parotid tumors based on CT images combined with stack generalization model. Med Biol Eng Comput 61, 3123–3135 (2023). https://doi.org/10.1007/s11517-023-02898-9

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  • DOI: https://doi.org/10.1007/s11517-023-02898-9

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