Removal of hair artifacts in skin lesion images and its impact on skin cancer detection

Early cancer diagnosis is one of the important factors in successful treatment and prognosis. In recent years, the incidence of skin cancer has risen globally, making early diagnosis critical for improving patient outcomes. This study focuses on improving skin cancer diagnosis by developing advanced hair removal filters that eliminate hair artefacts from dermoscopic images without compromising the integrity of skin lesion data. Traditional methods, such as the Dullrazor filter, have shown limitations in effectively removing both light and dark hairs, especially in images with dense hair coverage. In response, this research introduces novel hair removal algorithms that combine techniques like Wiener filtering, blackhat transformation, and adaptive thresholding, followed by image inpainting to restore the hair-removed areas. These methods have been tested on the HAM10000 dataset and compared with existing approaches in terms of their impact on skin lesion diagnosis accuracy. The results demonstrate that the newly developed filters significantly improve the accuracy of melanoma detection by preserving essential lesion features while effectively removing hair artefacts. Furthermore, the study highlights the potential of these enhanced pre-processing techniques to be integrated into deep learning models for skin cancer detection, offering a more reliable and efficient solution for early-stage diagnosis. The findings will contribute to the ongoing efforts to refine CAD systems emphasizing the importance of robust image pre processing in the context of medical image analysis.