DEEP LEARNING ALGORITHMS FOR OPTIMIZED THYROID NODULE CLASSIFICATION

Abstract

The increasing prevalence of thyroid cancer underscores the critical need for efficient classification and early detection of thyroid nodules. Automated systems can significantly aid physicians by expediting diagnostic processes. However, achieving this goal remains challenging due to limited medical image datasets and the complexity of feature extraction. This study addresses these challenges by emphasizing the extraction of meaningful features essential for tumor detection. The proposed approach integrates advanced techniques for feature extraction, enhancing the capability to classify thyroid nodules in ultrasound images. The classification framework includes distinguishing between benign and malignant nodules, as well as identifying specific suspicious classifications. The combined classifiers provide a comprehensive characterization of thyroid nodules, demonstrating promising accuracy in preliminary evaluations. These results mark a significant advancement in thyroid nodule classification methodologies. This research represents an innovative approach that could potentially offer valuable support in clinical settings, facilitating more rapid and accurate diagnosis of thyroid cancer.