An Efficient Approach for Skin Lesion Segmentation Using Dermoscopic Images: A Deep Learning Approach

Abstract

AbstractSegmentation is a process of detecting boundaries of an object to extract the object of interest within a given image. There are different techniques like CT scan, MRI scan, X-ray scan, and so on those can be used to get these medical images. Processing of these medical images is laborious because of variation in size and shape, and contrast. Hence, Skin lesion segmentation became a challenging task for researchers and dermatologists. The Segmentation of medical images plays a vital role in medical diagnosis and further treatment. Although there are many proposed image segmentation techniques, there is no perfect segmentation method that supports different datasets. This paper presents an efficient skin lesion segmentation model using a Convolutional Deconvolutional Neural Networks (CDNN). The proposed framework is developed based on Convolutional Neural Networks (CNN) by replacing the classification network with a segmentation network. The proposed model has used International Skin Imaging Collaboration (ISIC) 2017 challenging data set and PH2 dataset, and results are compared with State of Art models U-Net and SegNet.KeywordsSkin lesion segmentationConvolutional Neural Network(CNN)Dermoscopic imagesConvolutional Deconvolutional Neural Networks(CDNN)