Detector-segmentor network for skin lesion localization and segmentation

Shreshth Saini

Abstract:

Melanoma is a life-threatening form of skin cancer when left undiagnosed at the early stages.
Although there are more cases of non-melanoma cancer than melanoma cancer, melanoma cancer is
more deadly. Early detection of melanoma is crucial for the timely diagnosis of melanoma cancer
and prohibit its spread to distant body parts. Segmentation of skin lesion is a crucial step in the
classification of melanoma cancer from the cancerous lesions in dermoscopic images. Manual
segmentation of dermoscopic skin images is very time consuming and error-prone resulting in an
urgent need for an intelligent and accurate algorithm. In this study, we propose a simple yet novel
network-in-network convolution neural network(CNN) based approach for segmentation of the skin
lesion. A Faster Region-based CNN (Faster RCNN) is used for preprocessing to predict bounding
boxes of the lesions in the whole image which are subsequently cropped and fed into the segmentation
network to obtain the lesion mask. The segmentation network is a combination of the UNet and
Hourglass networks. We trained and evaluated our models on ISIC 2018 dataset and also crossvalidated on PH2
and ISBI 2017 datasets. Our proposed method surpassed the state-of-the-art with
Dice Similarity Coefficient of 0.915 and Accuracy 0.959 on ISIC 2018 dataset and Dice Similarity
Coefficient of 0.947 and Accuracy 0.971 on ISBI 2017 dataset