Enhancing Dermoscopic Skin Cancer Detection via Hair Artifact Removal: An Iterative Diffusion Model Approach

The elucidation and extrication of hair artifacts from dermoscopic images of potential skin malignancies emerged as a paramount technique in facilitating early skin cancer detection. Nevertheless, the prevailing literature lacks comprehensive exploration and study in this arena. The dearth of a benchmark dataset, coupled with the absence of an established device for early skin cancer detection, significantly hampers progress in this field. Hence, this article presents a novel synthetic dataset and introduces a cutting-edge deep learning model termed as the Iterative Diffusion Model (IDM). The HAM10000 dataset, encompassing seven distinct types of skin cancer devoid of any hair, is leveraged in our research. We amalgamate these skin cancer images with respective skin hair images to foster a more holistic understanding. The IDM we propose is predicated upon a series of iterative hair removal procedures, wherein noise is methodically incorporated in each successive iteration. The proficiency and efficacy of the IDM are empirically evaluated using a suite of quantitative metrics including Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index Measure (SSIM). In addition, histogram analysis is also adopted for assessment.