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A Cascade Classifier for Diagnosis of Melanoma in Clinical Images

Authors:

Peyman Sabouri , Hamid GholamHosseini, Thomas Larsson, John Collins

Research groups:

Biomedical Engineering
Ubiquitous Computing

Publication Type:

Conference/Workshop Paper

Venue:

The 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

Publisher:

IEEE

DOI:

10.1109/EMBC.2014.6945177

Abstract

Computer aided diagnosis of medical images can help physicians in better detecting and early diagnosis of many symptoms and therefore reducing the mortality rate. Realization of an efficient mobile device for semi-automatic diagnosis of melanoma would greatly enhance the applicability of medical image classification scheme and make it useful in clinical contexts. In this paper, interactive object recognition methodology is adopted for border segmentation of clinical skin lesion images. In addition, performance of five classifiers, KNN, Naïve Bayes, multi-layer perceptron, random forest and SVM are compared based on color and texture features for discriminating melanoma from benign nevus. The results show that a sensitivity of 82.6% and specificity of 83% can be achieved using a single SVM classifier. However, a better classification performance was achieved using a proposed cascade classifier with the sensitivity of 83.06% and specificity of 90.05% when performing ten-fold cross validation.

Bibtex

@inproceedings{Sabouri3619,
author = {Peyman Sabouri and Hamid GholamHosseini and Thomas Larsson and John Collins},
title = {A Cascade Classifier for Diagnosis of Melanoma in Clinical Images},
pages = {6748--6751},
month = {August},
year = {2014},
booktitle = {The 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society },
publisher = {IEEE},
url = {http://www.es.mdu.se/publications/3619-}
}