Name
Title | ||
|---|---|---|
PsLSNet: Automated psoriasis skin lesion segmentation using modified U-Net-based fully convolutional network |
Abstract | ||
|---|---|---|
•Implementation of U-Net based fully convolutional neural network (PsLSNet) for the automatic psoriasis skin lesion segmentation.•Validation of the proposed algorithm over a larger data set of 5241 images including challenging images.•Objective analysis of the proposed PsLSNet using five quantitative metrics (Dice coefficient, Accuracy, Jaccard Index, Specificity and Sensitivity).•Reliability of the proposed method is confirmed by varying the testing data size. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.bspc.2019.04.002 | Biomedical Signal Processing and Control |
Keywords | Field | DocType |
Psoriasis,Segmentation,Fully convolutional network,U-Net,Deep learning | Computer vision,Normalization (statistics),Pattern recognition,Lesion,Convolutional neural network,Segmentation,Sørensen–Dice coefficient,Fuzzy logic,Feature engineering,RGB color model,Artificial intelligence,Mathematics | Journal |
Volume | ISSN | Citations |
52 | 1746-8094 | 2 |
PageRank | References | Authors |
0.37 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Manoranjan Dash | 1 | 1886 | 98.15 |
| Narendra D. Londhe | 2 | 98 | 13.85 |
| Subhojit Ghosh | 3 | 24 | 9.71 |
| Ashish Semwal | 4 | 2 | 0.37 |
| Rajendra S. Sonawane | 5 | 52 | 4.66 |