Dermoscopic image lesion segmentation method based on deep separable convolutional network

Abstract

Aiming at the problem of the difficulty in locating the lesions in dermoscopic images and achieving precise segmentation of the lesions, a method of lesion segmentation in dermatological images based on deep separable convolutional network was proposed.Firstly, perform the black frame removal and hair removal processing on the dermoscopic image to remove the artificial and natural noise that hinders the location of the lesion in the image.Then the image after the noise reduction process was deformed and rotated to expand the data set.Finally, a encoder-decoder segmentation model based on depth separable convolution and hole convolution was constructed.The coding part extracts the features of the image, and the decoding part fuses the feature maps and restores the image details.Experimental results show that this method can achieve better segmentation results for the problem of skin disease image lesion segmentation.The accuracy of segmenting lesions reaches 95.24%.Compared with the segmentation model U-Net, the accuracy is improved by 6.17%.

Keywords

• dermoscopic image;lesion segmentation;hole convolution;deep separable convolution;encoder-decoder model