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• W4285280166 abstract "Skin cancer is a major health concern worldwide affecting the life and wellbeing of people. Melanoma is the most harmful skin cancer with the highest mortality rate. Screening and early detection of melanoma is a complicated task for dermatologists because of the huge variations in morphological features of skin lesions. There is a real need for an efficient and reliable automated diagnosis system that aids dermatologists in diagnosis and correct decision making. In this study, we have examined the state of art methods of image preprocessing, lesion segmentation, lesion feature extraction, and classification of dermoscopic images. This paper reports performance statistics of important machine learning and deep learning-based segmentation and classification methods. We also highlighted the challenges associated with melanoma detection." @default.
• W4285280166 created "2022-07-14" @default.
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• W4285280166 date "2022-01-01" @default.
• W4285280166 modified "2023-10-18" @default.
• W4285280166 title "Skin Cancer Detection: State of Art Methods and Challenges" @default.
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• W4285280166 doi "https://doi.org/10.1007/978-981-16-7985-8_76" @default.
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