ArticleMedical & biological engineering & computing2023
Classification of benign and malignant parotid tumors based on CT images combined with stack generalization model.
Article in Medical & biological engineering & computing, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed, 3 citations in OpenAlex.
- Automated segmentation and diagnosis of parotid tumors using a combined deep learning and radiomics model on T2-weighted MRI: a multi-vendor validation study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- MR Image Fusion-Based Parotid Gland Tumor Detection.Journal of imaging informatics in medicine · 2025Article
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Authors and funding
8 authors at 3 institutions in 1 country.
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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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