Authors:
Anwar Jimi
1
;
Hind Abouche
1
;
Nabila Zrira
2
and
Ibtissam Benmiloud
1
Affiliations:
1
MECAtronique Team, CPS2E Laboratory National Superior School of Mines Rabat, Morocco
;
2
ADOS Team, LISTD Laboratory National Superior School of Mines Rabat, Morocco
Keyword(s):
Skin Lesion Segmentation, DenseNet, Deep Learning, DenseUNet, Attention.
Abstract:
Skin lesion segmentation in dermoscopic images is still a challenging problem due to the blurry borders and low contrast of the lesions. Deep learning networks, like U-Net, have been successfully used to segment medical images over the past few years, and their performance has improved in terms of time and accuracy. This paper proposes an automated method for segmenting lesion boundaries that combines two architectures (i.e., the U-Net and the DenseNet as backbone) as well as the attention mechanism. Moreover, we also used adaptive gamma correction to enhance the contrast of the image, which considerably enhanced the segmentation results. Furthermore, we trained our model on the ISIC 2016, the ISIC 2017, and the ISIC 2018 datasets. Finally, the qualitative and quantitative experimental results of the skin lesion segmentation are very promising.