CBA generated receptive fields implemented in a facial expression recognition task

Authors:
Jose M. Jerez;Leonardo Franco;Ignacio Molina
Affiliations:
Escuela Técnica Superior de Ingeniería en Informática, Departamenlo de Lenguajes y Ciencias de la Computatión, Universidad de Málaga;Center for Computational Neuroscience, Department of Experimental Psychology, University of Oxford;Escuela Técnica Superior de Ingeniería en Telecomunicación, Departamcnto de Tecnología Electrónica, Universidad de Málaga
Venue:
IWANN'03 Proceedings of the Artificial and natural neural networks 7th international conference on Computational methods in neural modeling - Volume 1
Year:
2003

Citing 3
Cited 0

Infraclinic Breast Carcinoma: Application of Neural Networks Techniques for the Indication of Radioguided Biopsias

IWANN '97 Proceedings of the International Work-Conference on Artificial and Natural Neural Networks: Biological and Artificial Computation: From Neuroscience to Technology
Designing fuzzy inference systems from data: An interpretability-oriented review

IEEE Transactions on Fuzzy Systems
Fuzzy CoCo: a cooperative-coevolutionary approach to fuzzy modeling

IEEE Transactions on Fuzzy Systems

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Abstract

This paper presents an evolved rule-based tool for mammography interpretation denominated "COBRA: Catalonia online breastcancer risk assessor." COBRA is designed to aid radiologists in the interpretation of mammography to decide whether to perform a biopsy on a patient or not while providing a human-friendly explanation of the underlying reasoning. From a diagnostic point of view, the tool exhibits high performance measures (i.e., sensitivity, specificity, and positive predictive value). From an interpretability point of view, COBRA's behavior is explained by only 14 rules containing, in average, 2.73 conditions perrule.