Automated Framework for Segmenting and Identifying Thyroid Nodules in Ultrasound Images

Thyroid disease is among the most prevalent endocrine disorders, making early detection of thyroid nodules essential for effective management and treatment. The complex nature of thyroid nodules makes accurate detection challenging, even with advanced imaging techniques such as CT, MRI, and ultrasound. Traditional manual interpretation methods can be time-consuming and error-prone. This paper presents an automated framework that integrates advanced image processing techniques and machine learning approaches to enhance the detection and segmentation of thyroid nodules in ultrasound images. By employing the U-Net architecture alongside sophisticated image processing methods, the proposed system achieves a high accuracy rate of $99.1 \%$ on a large dataset. This framework not only accelerates and improves the accuracy of diagnoses but also reduces the workload for radiologists, representing a significant advancement in thyroid nodule detection.