Name
Abstract | ||
|---|---|---|
Melanoma is one the most increasing cancers since past decades. For accurate detection and classification, discriminative features are required to distinguish between benign and malignant cases. In this study, the authors introduce a fusion of structural and textural features from two descriptors. The structural features are extracted from wavelet and curvelet transforms, whereas the textural feat... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1049/iet-cvi.2017.0193 | IET Computer Vision |
Keywords | Field | DocType |
cancer,feature extraction,image fusion,image recognition,medical image processing,support vector machines,wavelet transforms | Pattern recognition,Local binary patterns,Support vector machine,Fusion,Sampling (statistics),Artificial intelligence,Classifier (linguistics),Discriminative model,Mathematics,Curvelet,Wavelet | Journal |
Volume | Issue | ISSN |
12 | 2 | 1751-9632 |
Citations | PageRank | References |
0 | 0.34 | 20 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Faouzi Adjed | 1 | 0 | 1.01 |
| Syed Jamal Safdar Gardezi | 2 | 2 | 1.70 |
| Fakhreddine Ababsa | 3 | 96 | 16.89 |
| ibrahima faye | 4 | 179 | 19.82 |
| Sarat Chandra Dass | 5 | 0 | 0.34 |