• Ms.T.Ramya
• I.Sneha Sandhya
• K.Poojitha
• T.Aishwarya
• I.S.Anisha

This study presents a novel method for hair artifact segmentation and removal in dermoscopic images using a combination of traditional image processing and U-Net deep learning architecture. The approach first uses techniques like thresholding, edge detection, and morphological operations to locate hair regions. A U-Net model is then trained to classify these regions as hair or non-hair. After identifying the hair, inpainting algorithms remove it while preserving skin texture. The method is evaluated on a diverse dataset and compared with existing techniques, showing high accuracy in hair removal and skin feature preservation. This technique enhances the quality of dermoscopic images, aiding more accurate skin condition diagnosis in dermatology.

HAIR SEGMENTATION AND REMOVAL IN DERMOSCOPIC IMAGES FOR ENHANCED SKIN CANCER DETECTION

"HAIR SEGMENTATION AND REMOVAL IN DERMOSCOPIC IMAGES FOR ENHANCED SKIN CANCER DETECTION ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 3, page no.e552-e559, March-2025, Available :http://www.jetir.org/papers/JETIR2503476.pdf

"HAIR SEGMENTATION AND REMOVAL IN DERMOSCOPIC IMAGES FOR ENHANCED SKIN CANCER DETECTION ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 3, page no. ppe552-e559, March-2025, Available at : http://www.jetir.org/papers/JETIR2503476.pdf