Name
Abstract | ||
|---|---|---|
Melanoma is the most deadly skin cancer. Early diagnosis is a challenge for clinicians. Current algorithms for skin lesions' classification focus mostly on segmentation and feature extraction. This article instead puts the emphasis on the learning process, testing the recognition performance of three different classifiers: support vector machine (SVM), artificial neural network and k‐nearest neighbor. Extensive experiments were run on a database of more than 5000 dermoscopy images. The obtained results show that the SVM approach outperforms the other methods reaching an average recognition rate of 82.5% comparable with those obtained by skilled clinicians. If confirmed, our data suggest that this method may improve classification results of a computer‐assisted diagnosis of melanoma. © 2010 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 20, 316–322, 2010 © 2010 Wiley Periodicals, Inc. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1002/ima.20261 | Int. J. Imaging Systems and Technology |
Keywords | Field | DocType |
average recognition rate,svm approach,classification focus,wiley periodicals,melanoma recognition,skilled clinicians,deadly skin cancer,recognition performance,early diagnosis,classification result,inc. int j imaging,kernel methods,support vector machines | Skin lesion,Computer science,Segmentation,Support vector machine,Skin cancer,Feature extraction,Artificial intelligence,Melanoma,Kernel method,Artificial neural network,Machine learning | Journal |
Volume | Issue | ISSN |
20 | 4 | 0899-9457 |
Citations | PageRank | References |
4 | 0.40 | 4 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Elisabetta La Torre | 1 | 12 | 1.44 |
| Barbara Caputo | 2 | 3298 | 201.26 |
| Tatiana Tommasi | 3 | 502 | 29.31 |