Abstract
Dealing with expert (human) knowledge consideration, the computer aided medical diagnosis dilemma is one of most interesting, but also one of the most difficult problems. Among difficulties contributing to the challenging nature of this problem, one can mention the need of fine classification. In this paper, we present a new classification approach founded on a tree like neural network based multiple-models structure, able to split a complex problem to a set of simpler sub-problems. This new concept has been used to design a Computer aided medical diagnostic tool that asserts auditory pathologies based on Brain-stem Evoked Response Auditory based biomedical test, which provides an ef-fective measure of the integrity of the auditory pathway.
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Rybnik, M., Diouf, S., Chebira, A., Amarger, V., Madani, K. (2004). Multi-neural Network Approach for Classification of Brainstem Evoked Response Auditory. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds) Artificial Intelligence and Soft Computing - ICAISC 2004. ICAISC 2004. Lecture Notes in Computer Science(), vol 3070. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24844-6_163
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DOI: https://doi.org/10.1007/978-3-540-24844-6_163
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22123-4
Online ISBN: 978-3-540-24844-6
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