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http://dx.doi.org/10.9718/JBER.2022.43.5.353

Research on the Lesion Classification by Radiomics in Laryngoscopy Image  

Park, Jun Ha (Department of Biomedical Eng., College of Health & Sci., Gachon University)
Kim, Young Jae (Department of Biomedical Eng., College of Health & Sci., Gachon University)
Woo, Joo Hyun (Department of Otorhinolaryngology-Head and Neck Surgery, Gachon University School of Medicine, Gil Medical Center)
Kim, Kwang Gi (Department of Biomedical Eng., College of Health & Sci., Gachon University)
Publication Information
Journal of Biomedical Engineering Research / v.43, no.5, 2022 , pp. 353-360 More about this Journal
Abstract
Laryngeal disease harms quality of life, and laryngoscopy is critical in identifying causative lesions. This study extracts and analyzes using radiomics quantitative features from the lesion in laryngoscopy images and will fit and validate a classifier for finding meaningful features. Searching the region of interest for lesions not classified by the YOLOv5 model, features are extracted with radionics. Selected the extracted features are through a combination of three feature selectors, and three estimator models. Through the selected features, trained and verified two classification models, Random Forest and Gradient Boosting, and found meaningful features. The combination of SFS, LASSO, and RF shows the highest performance with an accuracy of 0.90 and AUROC 0.96. Model using features to select by SFM, or RIDGE was low lower performance than other things. Classification of larynx lesions through radiomics looks effective. But it should use various feature selection methods and minimize data loss as losing color data.
Keywords
Radiomics; Machine learning; Laryngoscopy; Laryngeal disease; Quantitative;
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