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

(2 citation statements)

References 24 publications

(22 reference statements)

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“…Many studies have utilized wavelets for thyroid cancer detection, such as in [214], where wavelet techniques were employed to identify cancer regions in thyroid, breast, ovarian, and prostate tumors. In [215], texture information was used to diagnose TN malignancy through a two-level 2D wavelet transform. Other works exploring this area can be found in [216,217].…”

Section: Fe2 Texture Descriptionmentioning

confidence: 99%

AI in Thyroid Cancer Diagnosis: Techniques, Trends, and Future Directions

Habchi,

Himeur,

Kheddar

et al. 2023

Systems

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Artificial intelligence (AI) has significantly impacted thyroid cancer diagnosis in recent years, offering advanced tools and methodologies that promise to revolutionize patient outcomes. This review provides an exhaustive overview of the contemporary frameworks employed in the field, focusing on the objective of AI-driven analysis and dissecting methodologies across supervised, unsupervised, and ensemble learning. Specifically, we delve into techniques such as deep learning, artificial neural networks, traditional classification, and probabilistic models (PMs) under supervised learning. With its prowess in clustering and dimensionality reduction, unsupervised learning (USL) is explored alongside ensemble methods, including bagging and potent boosting algorithms. The thyroid cancer datasets (TCDs) are integral to our discussion, shedding light on vital features and elucidating feature selection and extraction techniques critical for AI-driven diagnostic systems. We lay out the standard assessment criteria across classification, regression, statistical, computer vision, and ranking metrics, punctuating the discourse with a real-world example of thyroid cancer detection using AI. Additionally, this study culminates in a critical analysis, elucidating current limitations and delineating the path forward by highlighting open challenges and prospective research avenues. Through this comprehensive exploration, we aim to offer readers a panoramic view of AI’s transformative role in thyroid cancer diagnosis, underscoring its potential and pointing toward an optimistic future.

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Section: Fe2 Texture Descriptionmentioning

confidence: 99%

AI in Thyroid Cancer Diagnosis: Techniques, Trends, and Future Directions

Habchi,

Himeur,

Kheddar

et al. 2023

Systems

View full text Add to dashboard Cite

show abstract

“…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

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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.

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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

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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

AI in Thyroid Cancer Diagnosis: Techniques, Trends, and Future Directions

AI in Thyroid Cancer Diagnosis: Techniques, Trends, and Future Directions

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

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