Name
Abstract | ||
|---|---|---|
In medical practice early accurate detection of all types of skin tumours is essential to guide appropriate management and improve patients' survival. The most important is to differentiate between malignant skin tumours and benign lesions. The aim of this research is classification of skin tumours by analyzing medical skin tumour dermoscopy images. This paper is focused on a new strategy based on hybrid model which combines mathematics and artificial techniques to define strategy to automatic classification for skin tumour images. The proposed hybrid system is tested on well-known HAM10000 data set, and experimental results are compared with similar researches. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/978-3-030-29859-3_61 | HYBRID ARTIFICIAL INTELLIGENT SYSTEMS, HAIS 2019 |
Keywords | Field | DocType |
Automatic classification, Dermoscopy images, Support Vector Machine, K-Nearest Neighbors, Multilayer perceptron | Pattern recognition,Computer science,Artificial intelligence | Conference |
Volume | ISSN | Citations |
11734 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Svetlana Simic | 1 | 40 | 12.78 |
| Svetislav Simic | 2 | 2 | 4.13 |
| Zorana Bankovic | 3 | 0 | 0.34 |
| Milana Ivkov-Simic | 4 | 0 | 0.34 |
| José Ramón Villar | 5 | 176 | 27.02 |
| Dragan Simic | 6 | 40 | 12.78 |