Color-invariant skin lesion semantic segmentation based on modified U-Net deep convolutional neural network.

Abstract

Melanoma is a type of skin lesion that is less common than other types of skin lesions, but it is fast growing and spreading. Therefore, it is classified as a serious disease that directly threatens human health and life. Recently, the number of deaths due to this disease has increased significantly. Thus, researchers are interested in creating computer-aided diagnostic systems that aid in the proper diagnosis and detection of these lesions from dermoscopy images. Relying on manual diagnosis is time consuming in addition to requiring enough experience from dermatologists. Current skin lesion segmentation systems use deep convolutional neural networks to detect skin lesions from RGB dermoscopy images. However, relying on RGB color model is not always the optimal choice to train such networks because some fine details of lesion parts in the dermoscopy images can not clearly appear using RGB color model. Other color models exhibit invariant features of the dermoscopy images so that they can improve the performance of deep neural networks. In the proposed Color Invariant U-Net (CIU-Net) model, a color mixture block is added at the beginning of the contracting path of U-Net. The color mixture block acts as a mixer to learn the fusion of various input color models and create a new one with three channels. Furthermore, a new channel-attention module is included in the connection path between encoder and decoder paths. This channel attention module is developed to enrich the extracted color features. From the experimental result, we found that the proposed CIU-Net works in harmony with the new proposed hybrid loss function to enhance skin segmentation results. The performance of the proposed CIU-Net architecture is evaluated using ISIC 2018 dataset and the results are compared with other recent approaches. Our proposed method outperformed other recent approaches and achieved the best Dice and Jaccard coefficient with values 92.56% and 91.40%, respectively.