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- W4361192309 abstract "Hyperspectral microscopic imaging (HMI) technology is a non-contact optical diagnostic method, which combines hyperspectral imaging (HSI) technology with microscopy to provide both spectral information and image information of the samples to be measured. In this paper, basal cell carcinoma (BCC), squamous cell carcinoma (SCC) and malignant melanoma (MM) were classified based on synthetic RGB image data from HMI cube by using four classification methods extreme learning machine (ELM), support vector machine (SVM), decision tree and random forest (RF). The highest classification accuracy of 0.791±0.060 and a KAPPA value of 0.685±0.095 were obtained when color moment, gray level co-occurrence matrix (GLCM) and local binary pattern (LBP) were used for image feature extraction, feature dimensions were reduced by the PLS, the sample sets were divided by the hold-out method, and the tissues were classified by the SVM model." @default.
- W4361192309 created "2023-03-31" @default.
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- W4361192309 date "2023-03-29" @default.
- W4361192309 modified "2023-09-29" @default.
- W4361192309 title "Classification of skin cancer based on hyperspectral microscopic imaging and machine learning" @default.
- W4361192309 doi "https://doi.org/10.1117/12.2666425" @default.
- W4361192309 hasPublicationYear "2023" @default.
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