Abstract
Skin cancer is quite common. Early detection is crucial for the treatment of skin cancer. Methods based on computer technology (deep learning, image processing) are now increasingly used to diagnose skin cancer. These methods can eliminate human error in the diagnostic process. Removing hair noise from lesion images is essential for accurate segmentation. A correctly segmented lesion image increases the success rate in diagnosing skin cancer. In this study, a new FCN8-based approach for hair removal and segmentation in skin cancer images is presented. Higher success was achieved by adding ResNetC to FCN8. ResNetC is a new model based on ResNet. Two datasets were used for the study: ISIC 2018 and PH2. Training success was 89.380% for hair removal and 97.050% for lesion segmentation. 3000 hair masks were created as part of the study to remove hair noise in the lesion images.
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