An Evaluation of Skin Lesion Segmentation Using Deep Learning Architectures

Abstract

Skin lesion segmentation for recognizing and defining the boundaries of skin lesions in images is proper for automated analysis of skin lesion images, especially for the early diagnosis and detection of skin cancers. Deep learning architectures are an efficient way to implement segmentation once a skin lesion dataset is provided with ground truth images. This study evaluates deep learning architectures on a hybrid dataset, including a private dataset collected from a hospital and a public ISIC dataset. Four different test cases exist in the analysis where the combinations of public and private datasets are used as train and test datasets. Experimental results include Unet, Unet++, DeepLabV3, DeepLabV3++, and FPN segmentation architectures. According to the comparative evaluations, mixed datasets, where public and private datasets were used together, provided the best results. The evaluations also show that the collected dataset with ground truth images provided promising results.

Keywords

• skin lesion segmentation
• deep learning architectures
• unet
• deeplabv3
• feature pyramids network