Name
Abstract | ||
|---|---|---|
Automatic diagnostics of skin lesions is an area of high interest. Identification of primary morphology in skin lesions could be a first step of an automatic diagnostics tool. We propose an end-to-end deep learning solution to the problem of classifying primary morphology images of types macule, nodule, papule and plaque. Experimental results show 0.775 accuracy on 4 classes and 0.8167 accuracy on 3 classes. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ICAIIC.2019.8668980 | 2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) |
Keywords | Field | DocType |
Skin,Lesions,Diseases,Morphology,Task analysis,Training,Deep learning | Anatomy,Skin lesion,Computer science,Artificial intelligence,Deep learning,Papule | Conference |
ISBN | Citations | PageRank |
978-1-5386-7822-0 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tatyana Polevaya | 1 | 0 | 0.34 |
| Roman Ravodin | 2 | 0 | 0.34 |
| Andrey Filchenkov | 3 | 46 | 15.80 |