Enhancing Dermoscopy Segmentation Accuracy: An Algorithmic Approach for Automatic Hair Removal for Skin Cancer Detection

Abstract

This paper presents an automated approach for hair removal in dermoscopy images to enhance the accuracy of skin cancer detection and segmentation. Traditional dermoscopic segmentation often faces challenges due to the presence of hair, which can generate false positives and obscure critical features of the lesion. This paper proposes an algorithm that effectively identifies and removes hair from skin cancer images, enabling clearer visualization of the skin's surface. This method combines image processing techniques with image reconstruction models to distinguish between hair and skin tissue, ensuring minimal distortion of the underlying skin features. The numerical results demonstrate significant improvements in the performance of automated skin cancer detection systems, highlighting the potential of this approach for clinical applications in dermatology.