A Comparative Study on Thyroid Nodule Classification using Transfer Learning Methods

The thyroid is an endocrine gland located in the front of the neck whose main purpose is to produce thyroid hormones necessary for the functioning of the entire body. Thyroid hormones may be produced too little or too much depending on dysfunction. Since the 1990s, there have been an increasing number of thyroid illness cases, and in recent years, thyroid cancer has become the malignancy with the fastest rate of increase. According to recent studies, thyroid dysfunction affects 42 million people in India. Much research has provided solutions for thyroid classification.

In this paper, we survey various transfer learning models to classify thyroid nodules and predict the best accuracy. Our study evaluated several models, including DenseNet169, ResNet101, and various EfficientNet variants, using a comprehensive dataset comprising 7,893 images. DenseNet169 achieved the highest accuracy at 95.96%, followed by ResNet101 and EfficientNetB1, with accuracies of 94.74% and 86.14%, respectively. The models were rigorously tuned and optimized using grid search strategies, with hyperparameters such as learning rate, batch size, optimizer type, and dropout rate carefully selected to enhance performance. The evaluation included precision, recall, and F1 score metrics, ensuring balanced performance across different metrics.

Our results demonstrate that advanced transfer learning models can distinguish malignancy from benign conditions with greater accuracy than traditional diagnostic approaches reliant on the human eye.

This research highlights the potential of integrating AI techniques in medical diagnostics to improve the accuracy and reliability of thyroid disease detection, ultimately leading to better patient outcomes.”