Skin Lesion Detection in Dermatological Images using Deep Learning
This paper demonstrates that it is possible to approach the skin lesion classification problem as a detection problem, a much more complex and interesting problem, by training a deep neural network based detection architecture and applying image processing techniques to a dermatology dataset as part of the data augmentation strategy with satisfactory and promising results. The image dataset used in the experiments comes from the ISIC Dermoscopic Archive, an open-access dermatology repository. In particular, the ISIC 2017 dataset, a subset of the ISIC archive, released for the annual ISIC challenge was used. We show that it is possible to adapt a high quality imaging dataset to the requirements demanded by a deep learning detection architecture such as YOLOv3. In conjunction with image processing techniques as a previous step, the deep neural network was successfully trained to identify and locate three different types of skin lesions.
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