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- W4295135551 abstract "Background and objective: According to the National Cancer Institute, among all malignant tumors, non-melanoma skin cancer, and melanoma are the most frequent in Brazil. Despite having a lower incidence, the melanoma type has accelerated growth and greater lethality. Several studies have been performed in recent years in the computer vision area to assist in the early diagnosis of skin cancer. Despite being widely used and presenting good results, deep learning approaches require a large amount of annotated data and considerable computational cost for training the model. Therefore, the present work explores active learning approaches to select a small set of more informative data for training the classifier. For that, different selection criteria are considered to obtain more effective and efficient classifiers for skin lesions." @default.
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- W4295135551 date "2022-11-01" @default.
- W4295135551 modified "2023-09-24" @default.
- W4295135551 title "Classification of Skin Lesion through Active Learning Strategies" @default.
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- W4295135551 doi "https://doi.org/10.1016/j.cmpb.2022.107122" @default.
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