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

ALFARO, ERICK; BOLAÑOS, FONSECA, XIMENA; ALBORNOZ, ENRIQUE M.; MARTÍNEZ, CÉSAR E.; CALDERÓN RAMÍREZ, SAÚL

Budapest

Conferencia; IEEE International Work Conference on Bioinspired Intelligence; 2019

EKIK University Research, Innovation and Service Center; Universidad de las Palmas de Gran Canaria

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 anddifferent 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.