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| MSCRHO-Net: A Deep Learning Model for Effective Removal of Hair Occlusion from DermoscopicImages |
| Du Hongxuan1, Liu Qingyi1, Ren Yande2, Wang Yan1, Zhang Yanan1, Bai Peirui1#* |
1(College of Electronic and Information Engineering, Shandong University of Science and Technology, Qingdao 266590, Shandong, China)
2(Department of Radiology, Affiliated Hospital of Qingdao University, Qingdao 265000,Shandong,China) |
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Abstract It is of great significance to carry out early detection and diagnosis of skin cancer based on automatic analysis of dermoscopic images. However, hair occlusion poses a challenge to image feature extraction and skin lesion diagnosis. In this paper, a multi-scale cascade deep learning model (MSCRHO-Net) by integrating the Laplace pyramid was proposed. First, Laplace pyramid was employed to extract the key features of different scales in image space. A cascade block was designed for each scale channel to predict the hairless image by a coarse to fine scheme. High precision hair extraction and boundaries details retention were achieved through this operation. Then, a combined loss function including perceptual loss and SSIM loss was constructed, which was helpful to enhance details recovery and obtain more clear hair removal images. The performance of MSCRHO-Net was validated on synthetic dataset and real dataset ISIC2017(4750 training images, 400 synthetic test images, and 223 real test images). The experimental results demonstrated that MSCRHO-Net was able to remove hair effectively without learning of huge parameters.The mean values of SSIM and PSNR reached 0.958 4 and 35.49 respectively, which significantly improved the performance (P<0.05) compared with other traditional hair removal methods. MSCRHO-Net shows high adaptability and robustness to complex hair structure, and can deal with complicated scenarios such as damaged lesion texture and blurred image.
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Received: 15 October 2023
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| About author:: #Member, Chinese Society of Biomedical Engineering |
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