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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.