A Segmentation Method and Comparison of Classification Methods for Thyroid Ultrasound Images

Singh, Nikita; Jindal, Alka

doi:10.5120/7818-1115

Cited by 15 publications

(10 citation statements)

References 14 publications

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“…In the last few years, the concept of artificial intelligence is used in the classification of thyroid disease. For the clinical examination, the machine learning algorithm has effectively implemented the interpretation of data related to thyroid disease and diagnosis of the disease at the early stage [11]. Singh [12] proposed the K-nearest neighbor and Support vector machine, Bayesian classification of thyroid diseases in the data set of the ultrasound image of thyroid nodules.…”

Section: Review Of Literaturementioning

confidence: 99%

Butterfly Optimized Feature Selection with Fuzzy C-Means Classifier for Thyroid Prediction

Kumar¹,

Parthasarathi²,

Masud³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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The main task of thyroid hormones is controlling the metabolism rate of humans, the development of neurons, and the significant growth of reproductive activities. In medical science, thyroid disorder will lead to creating thyroiditis and thyroid cancer. The two main thyroid disorders are hyperthyroidism and hypothyroidism. Many research works focus on the prediction of thyroid disorder. To improve the accuracy in the classification of thyroid disorder this paper proposes optimization-based feature selection by using differential evolution with the Butterfly optimization algorithm (DE-BOA). For the classifier fuzzy C-means algorithm (FCM) is used. The proposed DEBOA-FCM is evaluated with parametric metric measures of sensitivity, specificity, and accuracy. In this work, the thyroid disease dataset collected from the machine learning University of California Irvine (UCI) database was used. The accuracy rate for the Differential Evolutionary algorithm got 0.884, the Butterfly optimization algorithm got 0.906, Fuzzy C-Means algorithm got 0.899 and DEBOA + Focused Concept Miner (FCM) proposed work 0.943.

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Section: Review Of Literaturementioning

confidence: 99%

Butterfly Optimized Feature Selection with Fuzzy C-Means Classifier for Thyroid Prediction

Kumar¹,

Parthasarathi²,

Masud³

et al. 2023

Intelligent Automation &Amp; Soft Computing

View full text Add to dashboard Cite

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“…Nikita Sigh et. al [23], Support Vector Machine surpassed K-Nearest Neighbor and Bayesian with an accuracy of 84.62 percent. KNN independently discovered the closest neighborhood.…”

Section: Introductionmentioning

confidence: 96%

Prediction of Hypothyroidism and Hyperthyroidism Using Machine Learning Algorithms

Shama

Hossain

Adhikary

et al. 2022

Preprint

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The thyroid gland is the key organs in the human body, secreting two hormones that help to regulate the human body's metabolism. Thyroid disease is a severe medical complaint that could developed by high TSH (Thyroid Stimulating Hormone) levels or an infection in the thyroid tissues. Hypothyroidism and hyperthyroidism are two important conditions caused by insufficient thyroid hormone production and excessive thyroid hormone production, respectively. Machine learning model can utilize for precise processing of the data that is generated from different the medical sector and could be used for building a model for the prediction of several diseases. In this study, we used a variety of machine learning algorithm to predict hypothyroidism and hyperthyroidism. Moreover, we identified the most significant features, which can be used to detect thyroid diseases more precisely. After completing the preprocessing and feature selection steps, we applied our modified and original data to several classification models to predict thyroidism. Finally, we found Random Forest is giving the maximum score in all sectors like accuracy, precision, recall, F1 score in our dataset and Naive Bayes is performing very poorly. By analyzing the characteristics and behavior of the dataset, we can identify the most important features of the datasets. In terms of accuracy and other performance evaluation criteria, this study could advocate the use of effective classifiers and features backed by machine learning algorithms for the detection and diagnosis of thyroid disease.

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“…Several studies have been conducted to determine the efficacy of these approaches. For example, Singh in [11] used thyroid nodule ultrasound images to apply the K-nearest neighbors (KNN), support vector machines (SVM), and Bayesian classification. Erol et al [12] used a multilayer perceptron and radial basis function neural (MLPRBFN) network to classify thyroid disease structurally.…”

Section: Introductionmentioning

confidence: 99%

Effective Voting Ensemble of Homogenous Ensembling with Multiple Attribute-Selection Approaches for Improved Identification of Thyroid Disorder

et al. 2021

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Thyroid disease is characterized by abnormal development of glandular tissue on the periphery of the thyroid gland. Thyroid disease occurs when this gland produces an abnormally high or low level of hormones, with hyperthyroidism (active thyroid gland) and hypothyroidism (inactive thyroid gland) being the two most common types. The purpose of this work was to create an efficient homogeneous ensemble of ensembles in conjunction with numerous feature-selection methodologies for the improved detection of thyroid disorder. The dataset employed is based on real-time thyroid information obtained from the District Head Quarter (DHQ) teaching hospital, Dera Ghazi (DG) Khan, Pakistan. Following the necessary preprocessing steps, three types of attribute-selection strategies; Select From Model (SFM), Select K-Best (SKB), and Recursive Feature Elimination (RFE) were used. Decision Tree (DT), Gradient Boosting (GB), Logistic Regression (LR), and Random Forest (RF) classifiers were used as promising feature estimators. The homogeneous ensembling activated the bagging- and boosting-based classifiers, which were then classified by the Voting ensemble using both soft and hard voting. Accuracy, sensitivity, mean square error, hamming loss, and other performance assessment metrics have been adopted. The experimental results indicate the optimum applicability of the proposed strategy for improved thyroid ailment identification. All of the employed approaches achieved 100% accuracy with a small feature set. In terms of accuracy and computational cost, the presented findings outperformed similar benchmark models in its domain.

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A Segmentation Method and Comparison of Classification Methods for Thyroid Ultrasound Images

Cited by 15 publications

References 14 publications

Butterfly Optimized Feature Selection with Fuzzy C-Means Classifier for Thyroid Prediction

Butterfly Optimized Feature Selection with Fuzzy C-Means Classifier for Thyroid Prediction

Prediction of Hypothyroidism and Hyperthyroidism Using Machine Learning Algorithms

Effective Voting Ensemble of Homogenous Ensembling with Multiple Attribute-Selection Approaches for Improved Identification of Thyroid Disorder

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