Data/hora: 05/06/2024 18:27:15

Provedor de dados: 	52	
País: 	Brazil	
Título: 	A Novel Run-length based wavelet features for Screening Thyroid Nodule Malignancy	
Autores: 	Haji,Salih Omer; Yousif,Raghad Zuhair.	
Data: 	2019-01-01	
Ano: 	2019	
Palavras-chave: 	Thyroid Nodule; SVM and DT classifiers; Computer-aided iagnosis; Feature extraction; Run-Length Matrix.	
Resumo: 	Abstract: Thyroid nodules are cell growths in the thyroid which might be for in one of two categories benign or malignant. Nodular thyroid disease is common and because of the associated risk of malignancy and hyper-function; these nodules have to be examined thoroughly. Hence diagnosing thyroid nodule malignancy in the early stage can mitigate the possibility of death. This paper presents an intelligent thyroid nodules malignancy diagnosis using texture information in run-length matrix derived from 2- level 2D wavelet transform bands (approximation and details). In this work, ANOVA test has been used to for feature selection to reduce for feature selection about 45 run-length features with and without wavelet generated, before feeding those features which clinical importance to the Support Vector Machine(SVM) and Decision Tree (DT) classifier to perform the automated diagnosis. The validation of this work is activated using 100-thyroid nodule images spliced equally between the two categories (50 Benign and 50 Malignant). The proposed system can detect thyroid nodules malignancy with an average accuracy of about 97% using SVM classifier for the run- length matrix, features derived from spatial domain while the average accuracy is increased to 98% in case of hybrid feature derived from spatial domain and 2-level wavelet decomposition. For the other proposed classifier (DT), the average accuracy in case of spatial domain based features is 93% whereas the average accuracy of the hybrid features system is 97%. Hence the proposed system can be used for the screening of thyroid nodules.	
Tipo: 	Info:eu-repo/semantics/article	
Idioma: 	Inglês	
Identificador: 	http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1516-89132019000100608	
Editor: 	Instituto de Tecnologia do Paraná - Tecpar	
Relação: 	10.1590/1678-4324-2019170821	
Formato: 	text/html	
Fonte: 	Brazilian Archives of Biology and Technology  v.62 2019	
Fonte: 	info:eu-repo/semantics/openAccess