A Novel Technique for Detecting Various Thyroid Diseases Using Deep Learning

Prathibha, Soma; Dahiya, Deepak; Robin, C. R. Rene; Nishkala, Cherukuru Venkata; Swedha, S.

doi:10.32604/iasc.2023.025819

Cited by 10 publications

(1 citation statement)

References 18 publications

(21 reference statements)

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“…To identify five distinct thyroid conditions, hypothyroidism, hyperthyroidism, and thyroid cancer, a CNN-based modified ResNet architecture was used in another study [28].…”

Section: Related Workmentioning

confidence: 99%

Early Thyroid Risk Prediction by Data Mining and Ensemble Classifiers

Alshayeji

2023

MAKE

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Thyroid disease is among the most prevalent endocrinopathies worldwide. As the thyroid gland controls human metabolism, thyroid illness is a matter of concern for human health. To save time and reduce error rates, an automatic, reliable, and accurate thyroid identification machine-learning (ML) system is essential. The proposed model aims to address existing work limitations such as the lack of detailed feature analysis, visualization, improvement in prediction accuracy, and reliability. Here, a public thyroid illness dataset containing 29 clinical features from the University of California, Irvine ML repository was used. The clinical features helped us to build an ML model that can predict thyroid illness by analyzing early symptoms and replacing the manual analysis of these attributes. Feature analysis and visualization facilitate an understanding of the role of features in thyroid prediction tasks. In addition, the overfitting problem was eliminated by 5-fold cross-validation and data balancing using the synthetic minority oversampling technique (SMOTE). Ensemble learning ensures prediction model reliability owing to the involvement of multiple classifiers in the prediction decisions. The proposed model achieved 99.5% accuracy, 99.39% sensitivity, and 99.59% specificity with the boosting method which is applicable to real-time computer-aided diagnosis (CAD) systems to ease diagnosis and promote early treatment.

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“…To identify five distinct thyroid conditions, hypothyroidism, hyperthyroidism, and thyroid cancer, a CNN-based modified ResNet architecture was used in another study [28].…”

Section: Related Workmentioning

confidence: 99%

Early Thyroid Risk Prediction by Data Mining and Ensemble Classifiers

Alshayeji

2023

MAKE

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Detecting Thyroid Disease Using Optimized Machine Learning Model Based on Differential Evolution

Gupta,

Rustam,

Kanwal

et al. 2024

Int J Comput Intell Syst

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Thyroid disease has been on the rise during the past few years. Owing to its importance in metabolism, early detection of thyroid disease is a task of critical importance. Despite several existing works on thyroid disease detection, the problem of class imbalance is not investigated very well. In addition, existing studies predominantly focus on the binary-class problem. This study aims to solve these issues by the proposed approach where ten types of thyroid diseases are considered. The proposed approach uses a differential evolution (DE)-based optimization algorithm to fine-tune the parameters of machine learning models. Moreover, conditional generative adversarial networks are used for data augmentation. Several sets of experiments are carried out to analyze the performance of the proposed approach with and without model optimization. Results suggest that a 0.998 accuracy score can be obtained using AdaBoost with DE optimization which is better than existing state-of-the-art models.

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