A Novel Run-length based wavelet features for Screening Thyroid Nodule Malignancy

Haji, S O; Yousif, Raghad Zuhair

doi:10.1590/1678-4324-2019170821

Cited by 4 publications

(1 citation statement)

References 24 publications

(22 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A normalization has been applied on the input features sets before the feature selection stage. The features selection stage is considered as a final stage in feature extraction and processing procedure [18]. It is aimed to improve the performance of a classifier and achieve a minimum classification error.…”

Section: Features Extraction and Selectionmentioning

confidence: 99%

A Slantlet based Statistical Features Extraction for Classification of Normal, Arrhythmia, and Congestive Heart Failure in Electrocardiogram

Saeed

Yousif

2021

UHD J SCI TECH

Self Cite

View full text Add to dashboard Cite

Intelligent and automated systems for diagnosing heart disease play a key role in treatment of heart disease and hence mitigating the possibility of heart disease, heart failure or sudden death. Thus, a Computer-Aided Design CAD system can provide a doctors with a clue about the category of patient heart disease, which might be Normal Sinus Rhythm, Abnormal Arrhythmia (ARR), and Congestive Heart Failure (CHF) electrocardiogram (ECG) signal. In this work a novel Slantlet transform (SLT) statistical features have been extracted and selected for 900 ECG segments taken from MIT-BIH ARR Database equally from three classes mentioned above for heart dieses classification through ECG signals. Based on the superiority of SLT in time localization as compared to the traditional wavelet transform, 12 out of 14 statistical features have been successfully passed the ANOVA test with P-value of 10−3. Then after, the relevant features are provided to three well-known classifiers (Support Vector Machine [SVM], K-nearest neighbor, and Naive Bayes). The performance tests show that Attaining 99.254% classification average AUC it can be achieved using SLT transform based features along with SVM classifier, which is a set of related supervised machine learning algorithm used for regression and classification with high generalization ability. It performs classification on two group problems. SVM classifier determines the best hyperplane which distinguishes between each positive and negative training sample.

show abstract

Section: Features Extraction and Selectionmentioning

confidence: 99%

A Slantlet based Statistical Features Extraction for Classification of Normal, Arrhythmia, and Congestive Heart Failure in Electrocardiogram

Saeed

Yousif

2021

UHD J SCI TECH

Self Cite

View full text Add to dashboard Cite

show abstract

Integrate domain knowledge in training multi-task cascade deep learning model for benign–malignant thyroid nodule classification on ultrasound images

Yang

Dong

et al. 2021

Engineering Applications of Artificial Intelligence

View full text Add to dashboard Cite

Detection of benign and malignant thyroid nodules: an reinforcement region selection network framework

Zhang,

Liu,

Wang

2023

2023 5th International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI)

View full text Add to dashboard Cite

A Novel Run-length based wavelet features for Screening Thyroid Nodule Malignancy

Abstract: 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.

Cited by 4 publications

References 24 publications

A Slantlet based Statistical Features Extraction for Classification of Normal, Arrhythmia, and Congestive Heart Failure in Electrocardiogram

A Slantlet based Statistical Features Extraction for Classification of Normal, Arrhythmia, and Congestive Heart Failure in Electrocardiogram

Integrate domain knowledge in training multi-task cascade deep learning model for benign–malignant thyroid nodule classification on ultrasound images

Detection of benign and malignant thyroid nodules: an reinforcement region selection network framework

Contact Info

Product

Resources

About