Biologically inspired skin lesion segmentation using a geodesic active contour technique

2015 ◽  
Vol 22 (2) ◽  
pp. 208-222 ◽  
Author(s):  
R. Kasmi ◽  
K. Mokrani ◽  
R. K. Rader ◽  
J. G. Cole ◽  
W. V. Stoecker
Keyword(s):  
Skin Lesion ◽  
Active Contour ◽  
Lesion Segmentation ◽  
Biologically Inspired ◽  
Geodesic Active Contour ◽  
Contour Technique

scholarly journals FAC-Net: Feedback Attention Network Based on Context Encoder Network for Skin Lesion Segmentation

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5172
Author(s):  
Yuying Dong ◽  
Liejun Wang ◽  
Shuli Cheng ◽  
Yongming Li
Keyword(s):  
Skin Lesion ◽  
Real Life ◽  
Good Deal ◽  
Experimental Tests ◽  
Skin Lesions ◽  
Lesion Segmentation ◽  
Attention Network ◽  
Effective Feedback ◽  
Public Datasets ◽  
Learning Architectures

Considerable research and surveys indicate that skin lesions are an early symptom of skin cancer. Segmentation of skin lesions is still a hot research topic. Dermatological datasets in skin lesion segmentation tasks generated a large number of parameters when data augmented, limiting the application of smart assisted medicine in real life. Hence, this paper proposes an effective feedback attention network (FAC-Net). The network is equipped with the feedback fusion block (FFB) and the attention mechanism block (AMB), through the combination of these two modules, we can obtain richer and more specific feature mapping without data enhancement. Numerous experimental tests were given by us on public datasets (ISIC2018, ISBI2017, ISBI2016), and a good deal of metrics like the Jaccard index (JA) and Dice coefficient (DC) were used to evaluate the results of segmentation. On the ISIC2018 dataset, we obtained results for DC equal to 91.19% and JA equal to 83.99%, compared with the based network. The results of these two main metrics were improved by more than 1%. In addition, the metrics were also improved in the other two datasets. It can be demonstrated through experiments that without any enhancements of the datasets, our lightweight model can achieve better segmentation performance than most deep learning architectures.

scholarly journals Lightweight encoder-decoder model for automatic skin lesion segmentation

2021 ◽  
pp. 100640
Author(s):  
Adi Wibowo ◽  
Satriawan Rasyid Purnama ◽  
Panji Wisnu Wirawan ◽  
Hanif Rasyidi
Keyword(s):  
Skin Lesion ◽  
Lesion Segmentation
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