Use of Machine Learning and Deep Learning Algorithms the Diagnosis of Thyroid Nodules

Abstract

This research explores the effectiveness of machine learning and deep learning algorithms in evaluating thyroid nodules, with a particular focus on their ability to assist in making diagnostic decisions. Experimental studies evaluate the success of different models in classifying thyroid nodules.

The results include a detailed analysis based on Positive Predictive Value (PPV) and False Discovery Rate (FDR) values on benign, malignant and normal classes. Model-1 showed high performance in the malignant and normal classes, although there is potential for improvement in the benign class. Model-2 improved the performance in the benign and normal classes, but there is still room for improvement in the benign class.

In particular, Model-3 and Model-4 showed a successful classification performance, achieving high PPV values in all three classes. These models stand out with high PPV and low FDR values, especially in the benign class. These findings highlight the significant promise of deep learning algorithms in improving the assessment of thyroid nodules, playing a key role in improving diagnostic accuracy and clinical decision making.

However, considering the areas for improvement, it seems that more studies and improvements are needed, especially in the benign class. Future research should focus on the use of larger datasets and improved methodologies to improve model performance and use it more reliably in clinical applications.