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AuthorAl-Absi, Hamada R. H.
AuthorAbdullah, Azween
AuthorHassan, Mahamat Issa
AuthorShaban, Khaled Bashir
Available date2022-12-21T10:01:47Z
Publication Date2011
Publication NameCommunications in Computer and Information Science
ResourceScopus
URIhttp://dx.doi.org/10.1007/978-3-642-25453-6_12
URIhttp://hdl.handle.net/10576/37511
AbstractDisease diagnosis often involves acquiring medical images using devices such as MRI, CT scan, x-ray, or mammograms of patients' organs. Though many medical diagnostic applications have been proposed; finding subtle cancerous cells is still an issue because they are very difficult to be identified. This paper presents an architecture that utilizes a learning algorithm, and uses soft computing to build a medical knowledge base and an inference engine for classifying new images. This system is built on the strength of artificial neural networks, fuzzy logic, and genetic algorithms. These machine intelligence are combined in a complementary approach to overcome the weakness of each other. Moreover, the system also uses Wavelet Transform and Principal Component Analysis for pre-processing and feature to produce features to be used as input to the learning algorithm. 2011 Springer-Verlag.
Languageen
SubjectArtificial Neural Networks
Computer Aided Diagnosis
Fuzzy Logic
Genetic Algorithms
Soft Computing
TitleHybrid intelligent system for disease diagnosis based on artificial neural networks, fuzzy logic, and genetic algorithms
TypeConference Paper
Pagination128-139
Issue NumberPART 2
Volume Number252 CCIS
dc.accessType Abstract Only


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