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This book provides a comprehensive treatment of methodologies underlying fuzzy logic, genetic algorithm, artificial neural networks and neuro-fuzzy hybrid for solving medical diagnostic problems like cancer, skin disease etc. Expert systems have been built to perform clinical decision-making functions, but the knowledge rules extracted from human experts generally have uncertain and ambiguous characteristics. To handle uncertainties in symptoms, descriptions and data, fuzzy logic is used with neural networks. Artificial neural networks provide aspects such as learning, adaptation and generalization that aid fuzzy logic inference under cognitive uncertainty. The neuro-fuzzy inference engine uses a weighed average of the premise and consequent parameters with the fuzzy rule serving as node and fuzzy sets representing the weight of the nodes. The image from which feature is to be extracted must be preprocessed so that significant features are not disturbed and then neuro-fuzzy-genetic hybrid can be used for making decisions. This monograph will appeal to students, researchers and R&D professionals who need the state-of-art introduction into this challenging and exciting young field.