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.