A Multi-path CNN for automated skin lesion segmentation

Abstract

Automatic skin lesion segmentation in dermoscopic images is an essential requirement for making the computer-aided diagnosis (CADs) system, but efficiently segmenting skin lesions by using automated methods is not easy due to the factors such as color variations, illumination variations, presence of hair, etc. Researchers have recently been exploring deep convolutional neural networks (CNN) based methods in this domain. In this paper, we present a new and effective multi-path deep CNN method for automated skin lesion segmentation. The proposed network uses an encoder network in the first path and uses the learning representation from a pretrained base model's intermediate layers in its second path, for better learning of the features at different levels. Also, we use Instance Normalization that makes the network adaptive for each image and alleviates the problem that occurs due to different intensity images. Our method does not require any pre- or post- processing of the input dermoscopic images, except resizing in the beginning. The comparative performance evaluation of the proposed method is performed by considering two benchmark datasets: ISBI-2016 and ISBI-2017 and commonly used evaluation metrics including jaccard index and dice coefficient. The results analysis demonstrates the effectiveness of our approach, with our method achieving better performance in comparison to existing state-of-the-art methods overall and also on melanoma and non-melanoma images, across both the datasets.