Name
Title | ||
|---|---|---|
Melanoma lesion detection and segmentation using deep region based convolutional neural network and fuzzy C-means clustering. |
Abstract | ||
|---|---|---|
In contrast with state of the art systems, the RCNN is capable to compute deep features with amen representation of Melanoma, and hence improves the segmentation performance. The RCNN can detect features for multiple skin diseases of the same patient as well as various diseases of different patients with efficient training mechanism. Series of experiments towards Melanoma detection and segmentation validates the effectiveness of our method. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.ijmedinf.2019.01.005 | International Journal of Medical Informatics |
Keywords | Field | DocType |
Melanoma segmentation,Region proposal,RCNN,Fuzzy C-Means,CAD tool | Data mining,Pattern recognition,Convolutional neural network,Segmentation,Skin cancer,Fuzzy logic,Jaccard index,Artificial intelligence,Pixel,Deep learning,Cluster analysis,Medicine | Journal |
Volume | ISSN | Citations |
124 | 1386-5056 | 6 |
PageRank | References | Authors |
0.49 | 31 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nudrat Nida | 1 | 6 | 0.49 |
| Aun Irtaza | 2 | 80 | 9.32 |
| Ali Javed | 3 | 14 | 2.62 |
| Muhammad Yousaf | 4 | 81 | 15.98 |
| Muhammad Tariq Mahmood | 5 | 90 | 14.01 |