| Enhancing Dermoscopic Skin Cancer Detection via Hair Artifact Removal: An Iterative Diffusion Model Approach |
| Paper ID : 1122-ICEEM2023 (R1) |
| Authors: |
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Anas M Ali *1, El- Sayed M. El- Rabaie2, Khalil Ramadan3, Prof. Fathi Sayed4 1Robotics and Internet-of-Things Laboratory, Prince Sultan University, Riyadh 12435, Saudi Arabia 2communication engineering, faculty of electronic engineering, menofia university, menouf, menofia 3dept. of Electronics and Electrical Communications Faculty of Electronic Engineering, Menoufia University 4Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufa University, Menouf 32952, Egypt |
| Abstract: |
| The elucidation and extrication of hair artifacts from dermoscopic images of potential skin malignancies has 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 scholarly article proposes 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 model 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 model are empirically evaluated using a suite of quantitative measures including Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), and histogram analysis. |
| Keywords: |
| Hair Removal, Diffusion Model, IDM, PSNR, SSIM |
| Status : Paper Accepted |
