A Brief Analysis of U-Net and Mask R-CNN for Skin Lesion Segmentation

A brief analysis on the use of two deep neural architectures, the U-Net and Mask R-CNN for the segmentation of skin lesions in dermoscopic images is presented. The two systems were adapted to use the dataset provided by the International Skin Imaging Collaboration (ISIC) for its 2017 challenge and d...

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Bibliographic Details
Published in:	2019 IEEE International Work Conference on Bioinspired Intelligence (IWOBI) pp. 000123 - 000126
Main Authors:	Alfaro, Erick, Fonseca, Ximena Bolanos, Albornoz, Enrique M., Martinez, Cesar E., Ramrez, Saul Calderon
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01-07-2019
Subjects:	deep learning dermoscopic images melanoma segmentation skin lesions
Online Access:	Get full text
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Summary:	A brief analysis on the use of two deep neural architectures, the U-Net and Mask R-CNN for the segmentation of skin lesions in dermoscopic images is presented. The two systems were adapted to use the dataset provided by the International Skin Imaging Collaboration (ISIC) for its 2017 challenge and different experiments were carried out. Results showed that the Mask-R-CNN obtained better performance than U-Net, also with lower computation times, being a feasible architecture to further analysis and application also to skin lesion classification.
DOI:	10.1109/IWOBI47054.2019.9114436