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Saumya Salian, Sudhir Sawarkar
Pág. 59 - 72
The rise of incidences of melanoma skin cancer is a global health problem. Skin cancer, if diagnosed at an early stage, enhances the chances of a patient?s survival. Building an automated and effective melanoma classification system is the need of the ho...
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Damilola A. Okuboyejo and Oludayo O. Olugbara
The conventional dermatology practice of performing noninvasive screening tests to detect skin diseases is a source of escapable diagnostic inaccuracies. Literature suggests that automated diagnosis is essential for improving diagnostic accuracies in med...
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Long Hoang, Suk-Hwan Lee, Eung-Joo Lee and Ki-Ryong Kwon
Skin lesion classification has recently attracted significant attention. Regularly, physicians take much time to analyze the skin lesions because of the high similarity between these skin lesions. An automated classification system using deep learning ca...
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Flavia Grignaffini, Francesco Barbuto, Lorenzo Piazzo, Maurizio Troiano, Patrizio Simeoni, Fabio Mangini, Giovanni Pellacani, Carmen Cantisani and Fabrizio Frezza
Skin cancer (SC) is one of the most prevalent cancers worldwide. Clinical evaluation of skin lesions is necessary to assess the characteristics of the disease; however, it is limited by long timelines and variety in interpretation. As early and accurate ...
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Felicia Anisoara Damian, Simona Moldovanu, Nilanjan Dey, Amira S. Ashour and Luminita Moraru
(1) Background: In this research, we aimed to identify and validate a set of relevant features to distinguish between benign nevi and melanoma lesions. (2) Methods: Two datasets with 70 melanomas and 100 nevi were investigated. The first one contained ra...
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