Automatic skin lesion boundary segmentation using deep learning convolutional networks with weighted cross entropy

Abstract:

An automatic segmentation of skin lesions from the dermoscopy images is a key procedure to accurately diagnose different skin diseases. In this study, recent state-of-the-art deep learning segmentation models which are U-Net, SegNet, FCN, and FrCN utilizing weighted cross entropy as a loss function are adapted and utilized. We evaluated all these different models using the ISIC 2018 segmentation challenge dataset. The U-Net, SegNet, FCN, and FrCN methods achieve average threshold Jaccard index of 54.4%, 69.5%, 74.7%, and 74.6% on the online validation dataset, respectively. These segmentation methods generate fine boundaries of the segmented lesions.

Año de publicación:

2018

Keywords:

Fuente:

google