Abstract / truncated to 115 words (read the full abstract)

Cancer, with its varying and hard to detect types, became one of the most dangerous diseases for humans. Melanoma is a type of skin cancer that has the most mortality rate among its type. The usual melanoma detection process is based on awareness of the patient and the experience of the visual investigator. Even though the invention of dermoscopes reduce its effects, “subjectivity” problem plays a huge role on the detection accuracy, which creates a need for automated detection. In this thesis, history of automated melanoma detection on dermoscopic images and caveats of present frameworks are studied. Different approaches to overcome these caveats are explored. As a result, a new melanoma detection algorithm based on ... toggle 4 keywords

melanoma detection – bag of visual words – neural networks – ISIC

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