Abstract
Melanoma is considered one of the most dangerous skin cancer diseases that threaten human health and life. Early diagnosis of melanoma is a big challenge, especially with the presence of color variations across similar lesion types. Automatic skin lesion segmentation is an essential step to build a successful skin disease classification system. Recent deep learning architectures significantly improve the skin lesion segmentation results. Especially, U-Net deep convolutional neural network (CNN) is considered one of the state-of-the-art models with promising performance. Most deep CNNs and particularly U-Net model utilize a single input RGB color image for skin lesion semantic segmentation. However, RGB color space is not usually the best choice to represent the invariant characteristics of skin lesion chromatic information. The selection of the optimal color space significantly affects the performance of segmentation results. In this paper, three novel variants of U-Net model with single, dual, and triple inputs, namely, Single Input Color U-Net (SICU-Net), Dual Input Color U-Net (DICU-Net) and Triple Input Color U-Net (TICU-Net) are proposed. The structure of SICU-Net, DICU-Net, and TICU-Net contains single, dual, and triple encoder sub-networks connected with only a single decoder path. Each encoder sub-network is fed with different color space of the input image. A channel-wise attention module is utilized to fuse the contribution of the learned feature maps from each encoder sub-network which is fed to the decoder sub-network to generate segmented image map. Moreover, a composite loss function is designed to improve the performance of the proposed CU-Net models. Three public benchmark datasets, namely, International Skin Imaging Collaboration (ISIC 2017, ISIC 2018) and PH2 datasets, are utilized to evaluate the performance of the proposed models. Experimental results reveal that the proposed models significantly improve the performance of the original U-Net model and achieve comparable performance with other state-of-the-art methods.
Highlights
S KIN cancer occurs when some skin cells are grown abnormally
International Skin Imaging Collaboration ISIC dataset 2017 1 contains three subsets, the first training subset has 2000 lesion images in JPG format divided into 3 subcategories, 1372 images for benign images, 374 for Melanoma images, and 254 for Seborrheic_Keratosis (SK) and their 2000 corresponding binary mask images in PNG format
The results show that all evaluation metrics are improved and the best ACC,Dice coefficient (DIC) and Jaccard index (JAC) are achieved with YCbCr color space, the best TNR is yielded from YIQ color space, and the best TPR is yielded from RGB color space
Summary
S KIN cancer occurs when some skin cells are grown abnormally. It is commonly appeared in areas of the body that are most exposed to ultraviolet (UV) radiation [1]. Skin cancer is divided into three major types, namely, squamous cell carcinoma, basal cell carcinoma, and melanoma [2]. Melanoma is the most dangerous type of skin cancer since it appears and grows in the melanocyte cells that produce melanin [3]–[5]. Like many diseases, is vulnerable to human error or it may be costly. Skin lesion image analysis is an essential stage for early skin cancer disease diagnosis. Skin Lesion segmentation is a challenging problem
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