Experimental results demonstrate improved accuracy and diagnostic potential for thyroid disorders using deep learning-based methods.
The experimental results of the proposed method were compared with contemporary methods and illustrate relatively better performance in terms of accuracy, sensitivity, precision, and F1-score. The value of the kappa coefficient, 0.9148, also depicts that the proposed method has the potential for applicability in clinical diagnosis to assist physicians in assessing the accurate type of thyroid disorders.
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Ghani et al. (2026) studied this question.
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