DOI: 10.18586/msufbd.1884262 ISSN: 2147-7930

Classification of Thyroid Nodules from Ultrasound Images Using Deep Learning Methods

Mehmet Emre Özbey, Mehmet Kaplan, Sercan Yalçın, Muhammed Yıldırım
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Thyroid cancer is one of the most prevalent malignancies of the endocrine system, and early diagnosis is critical for improving clinical outcomes. Although ultrasound imaging is the primary modality for thyroid nodule assessment, diagnostic interpretation remains highly dependent on expert experience. This study presents a standardized benchmarking framework for the systematic comparison of state-of-the-art deep learning architectures on thyroid ultrasound images. A total of 15 models, including RegNetY-032, ResNet50, EfficientNet, Inception, and YOLOv8 classification variants, were evaluated on the TN5000 dataset consisting of 5,000 images under identical preprocessing and training conditions. Performance was assessed using Accuracy, Precision, Recall, F1-score, and AUROC metrics. Experimental results showed that Inception-v3 achieved the highest diagnostic accuracy (0.984) and F1-score (0.9263), while RegNetY-032 demonstrated the strongest discriminative capability with an AUROC of 0.941. Supported by Grad-CAM-based explainability and error analyses, the proposed framework provides a reproducible and clinically interpretable evaluation of deep learning models for thyroid nodule classification.

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