Thyroid Nodules Classification using Weighted Average Ensemble and DCRITIC Based TOPSIS Methods for Ultrasound Images

Abstract

Background: Thyroid disorders are prevalent worldwide and impact many people. The abnormal growth of cells in the thyroid gland region is very common and even found in healthy people. These abnormal cells can be cancerous or non-cancerous, so early detection of this disease is the only solution for minimizing the death rate or maximizing a patient's survival rate. Traditional techniques to detect cancerous nodules are complex and timeconsuming; hence, several imaging algorithms are used to detect the malignant status of thyroid nodules timely. Aim: This research aims to develop computer-aided diagnosis tools for malignant thyroid nodule detection using ultrasound images. This tool will be helpful for doctors and radiologists in the rapid detection of thyroid cancer at its early stages. The individual machine learning models are inferior to medical datasets because the size of medical image datasets is tiny, and there is a vast class imbalance problem. These problems lead to overfitting; hence, accuracy is very poor on the test dataset. Objective: This research proposes ensemble learning models that achieve higher accuracy than individual models. The objective is to design different ensemble models and then utilize benchmarking techniques to select the best model among all trained models. Methods: This research investigates four recently developed image transformer and mixer models for thyroid detection. The weighted average ensemble models are introduced, and model weights are optimized using the hunger games search (HGS) optimization algorithm. The recently developed distance correlation CRITIC (D-CRITIC) based TOPSIS method is utilized to rank the models. Results: Based on the TOPSIS score, the best model for an 80:20 split is the gMLP + ViT model, which achieved an accuracy of 89.70%, whereas using a 70:30 data split, the gMLP + FNet + Mixer-MLP has achieved the highest accuracy of 82.18% on the publicly available thyroid dataset. Conclusion: This study shows that the proposed ensemble models have better thyroid detection capabilities than individual base models for the imbalanced thyroid ultrasound dataset.