Automatic Classification of Thyroid Tumors for Women Based on Artificial Intelligence Models for Ultrasound Scans

Abstract

Thyroid cancer arises in the thyroid gland when its cells begin to grow uncontrollably. The thyroid gland is essential for producing hormones that regulate metabolism, heart rate, blood pressure, and body temperature. Thyroid cancer, characterized by uncontrolled cellular growth in the thyroid gland, poses significant health risks. This study presents a novel diagnostic model for distinguishing benign and malignant thyroid tumors in ultrasound images by integrating a transferred EfficientNetB0 model with a new parallel deep convolutional neural network (CNN). The methodology involves preprocessing using Anisotropic Diffusion Filtering (ADF) for noise reduction, followed by feature extraction via deep CNNs. A refined classification model, developed through feature selection and dimensionality reduction, is trained and validated using a dataset of 1137 ultrasound images. The proposed system achieves an accuracy of 92.28% and an F1-score of 92.76%, demonstrating its effectiveness in assisting clinical diagnosis. Comparative complexity analysis further validates its robustness in addition to visual analysis tool (spider graph) that provides additional insights. The results demonstrate the potential of deep learning (DL) models in improving the reliability of thyroid cancer diagnosis, aiding clinicians in decision-making processes and reducing the risk of misdiagnosis.