Title :
Comparison of Several ANN Architectures on the Thyroid Diseases Grades Diagnosis
Author :
Rouhani, Modjtaba ; Mansouri, Kamran
Author_Institution :
Islamic Azad Univ., Gonabad, Iran
Abstract :
Nowadays, with advancement of technology and science and expansion of computer usage in high-tech calculations, especially in the field of medicine, intelligence systems and in particular ANN are becoming of significant importance in automatic diagnosis and prognoses of different diseases. In this article, we have used several ANN architectures (namely RBF, PNN, LVQ) and SVMs, diagnosing thyroid diseases. As the degree of disease development is a critical parameter in medical treatment, we design those networks to classify the grade of diseases, too. The performance of each of them has studied and the best method is selected for each of classification tasks. The overall accuracy of diagnosis system is near 99%.
Keywords :
biological organs; diseases; medical diagnostic computing; patient diagnosis; probability; radial basis function networks; regression analysis; support vector machines; LVQ network; automatic diagnosis; disease grades; generalized regression neural network; hepatitis; medical treatment; probabilistic neural networks; radial basis functions; support vector machines; thyroid diseases; Artificial neural networks; Computer architecture; Diseases; Neural networks; Neurons; Pathology; Springs; Support vector machine classification; Support vector machines; Testing; GRNN; LVQ; PNN; RBF; SVM; Thyroids diseases;
Conference_Titel :
Computer Science and Information Technology - Spring Conference, 2009. IACSITSC '09. International Association of
Conference_Location :
Singapore
Print_ISBN :
978-0-7695-3653-8
DOI :
10.1109/IACSIT-SC.2009.24
