Comprehensive Analysis of Machine Learning Techniques for Thyroid Cancer Detection

Thyroid nodules are commonly identified in clinical practice, and although the majority are benign, a sizable portion may be malignant, requiring prompt and precise diagnosis. It is essential to distinguish between benign and malignant nodules in order to direct suitable clinical interventions and steer clear of needless procedures. This study proposes a comprehensive MATLAB-based system for ultrasound imaging-based thyroid nodule detection and classification. This study offers a multi-step process to enhance the quality of US thyroid images and highlight relevant structures, beginning with image preprocessing techniques like edge identification, contrast enhancement, and noise reduction. In order to find any patterns that stand out, the morphological and textural characteristics of the nodules are examined in the feature extraction step that follows. Finally, using machine learning techniques, the nodules are categorized as either benign or malignant. The objective of this classification is to improve clinical decision-making by offering a dependable, automated tool that facilitates risk assessment and early detection in the treatment of thyroid illness.