Abstract
In this paper we present a clinical decision support system for melanoma diagnosis. Unlike other systems based on diagnosis obtained just from one image, in this work it is employed an image set, that represents the evolution of damaged tissues, taken in different instances of time (for example once a month). Therefore, the system analyses the image sequence extracting the affected area and using the gradient orientations histogram of each area to compose a description which allows achieving a decision about the input. Hidden Markov Models are proposed as classify method, obtaining classification rates of 77%.
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Berenguer, V.J., Ruiz, D., Soriano, A. (2009). Application of Hidden Markov Models to Melanoma Diagnosis. In: Corchado, J.M., Rodríguez, S., Llinas, J., Molina, J.M. (eds) International Symposium on Distributed Computing and Artificial Intelligence 2008 (DCAI 2008). Advances in Soft Computing, vol 50. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85863-8_42
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DOI: https://doi.org/10.1007/978-3-540-85863-8_42
Publisher Name: Springer, Berlin, Heidelberg
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