Semantic Segmentation of Lesions from Dermoscopic Images using Yolo-DeepLab Networks

Abstract

Accurate segmentation of lesions from dermoscopic images is very important for timely diagnosis and treatment of skin cancers. Due to the variety of shapes, sizes, colors, and locations of lesions in dermoscopic images, automatic segmentation of skin lesions remains a challenge. In this study, a two-stage method for the segmentation of skin lesions based on deep learning is presented. In the first stage, convolutional neural networks (CNNs) estimate the approximate size and location of the lesion. A sub-image around the estimated bounding box is cropped from the original image. The sub-image is resized to an image of a predefined size. In order to segment the exact area of the lesion from the normal image, other CNNs are used in the DeepLab structure. The accuracy of the normalization stage has a significant impact on the final performance. In order to increase the normalization accuracy, a combination of four networks in the structure of Yolov3 is used. Two approaches are proposed to combine Yolov3 structures. The segmentation results of two networks in the DeepLab v3+ structure are also combined to improve the performance of the second stage. Another challenge is the small number of training images. To overcome this problem, the data augmentation is used, as well as using different modes of an image in each stage. In order to evaluate the proposed method, experiments are performed on the well-known ISBI 2017 dataset. Experimental results show that the proposed lesion segmentation method outperforms the state-of-the-art methods.