Exploiting Classifier Combination for Early Melanoma Diagnosis Support
Abstract
Melanoma is the most dangerous skin cancer and early diagnosis is the main factor for its successful treatment. Experienced dermatologists with specific training make the diagnosis by clinical inspection and they reach 80% level of both sensitivity and specificity. In this paper, we present a multi-classifiers system for supporting the early diagnosis of melanoma. The system acquires a digital image of the skin lesion and extracts a set of geometric and colorimetric features. The diagnosis is performed on the vector of features by integrating with a voting schema the diagnostic outputs of three different classifiers: discriminant analysis, k-nearest neighbor and decision tree. The system is build and validated on a set of 152 skin images acquired via D-ELM. The results are comparable or better of the diagnostic response of a group of expert dermatologists.
Cite
Text
Blanzieri et al. "Exploiting Classifier Combination for Early Melanoma Diagnosis Support." European Conference on Machine Learning, 2000. doi:10.1007/3-540-45164-1_7Markdown
[Blanzieri et al. "Exploiting Classifier Combination for Early Melanoma Diagnosis Support." European Conference on Machine Learning, 2000.](https://mlanthology.org/ecmlpkdd/2000/blanzieri2000ecml-exploiting/) doi:10.1007/3-540-45164-1_7BibTeX
@inproceedings{blanzieri2000ecml-exploiting,
title = {{Exploiting Classifier Combination for Early Melanoma Diagnosis Support}},
author = {Blanzieri, Enrico and Eccher, Claudio and Forti, Stefano and Sboner, Andrea},
booktitle = {European Conference on Machine Learning},
year = {2000},
pages = {55-62},
doi = {10.1007/3-540-45164-1_7},
url = {https://mlanthology.org/ecmlpkdd/2000/blanzieri2000ecml-exploiting/}
}