Abstract
Although computer aided diagnosis of melanoma is an active research area for more than two decades, its clinical application is still just on horizon. To speed up its clinical application, two critical challenges need to be solved: the data gap and the decision-making gap. Ideally, these two issues shall be attacked simultaneously. However, in the literature, most current methods designing melanoma diagnosis classifiers adopt a biased approach by either focusing on the data gap or on the decision-making gap while neglecting the other. In this article, we present one prototype system covering both the data gap and the decision-gap. Performance of this new method is presented and comparisons with respect to alternative approaches, including the conventional one, are also included.
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Zhou, Y., Song, Z. (2013). Binary Decision Trees for Melanoma Diagnosis. In: Zhou, ZH., Roli, F., Kittler, J. (eds) Multiple Classifier Systems. MCS 2013. Lecture Notes in Computer Science, vol 7872. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38067-9_33
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DOI: https://doi.org/10.1007/978-3-642-38067-9_33
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38066-2
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