%R 10.1007/978-3-642-25453-6₁₂
%N PART 2
%D 2011
%J Communications in Computer and Information Science
%L scholars1460
%O cited By 10; Conference of International Conference on Informatics Engineering and Information Science, ICIEIS 2011 ; Conference Date: 14 November 2011 Through 16 November 2011; Conference Code:87535
%X Disease 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.
%K Cancerous cells; Computer aided; CT scan; Disease diagnosis; Hybrid intelligent system; Machine intelligence; Medical diagnostics; Medical images; Medical knowledge; Pre-processing, Computer aided diagnosis; Computerized tomography; Fuzzy neural networks; Genetic algorithms; Inference engines; Information science; Intelligent systems; Knowledge based systems; Learning algorithms; Magnetic resonance imaging; Medical imaging; Neural networks; Principal component analysis; Soft computing; Wavelet transforms, Fuzzy logic
%P 128-139
%C Kuala Lumpur
%V 252 CC
%A H.R.H. Al-Absi
%A A. Abdullah
%A M.I. Hassan
%A K. Bashir Shaban
%T Hybrid intelligent system for disease diagnosis based on artificial neural networks, fuzzy logic, and genetic algorithms