Derin Öğrenme Yöntemleri Kullanılarak Ultrason Görüntülerinden Tiroid Nodüllerinin Sınıflandırılması

Abstract

Tiroid kanseri, endokrin sistemin en yaygın malignitelerinden biridir ve erken tanı klinik tedavi başarısı açısından kritik öneme sahiptir. Tiroid nodüllerinin değerlendirilmesinde ultrason görüntüleme temel yöntem olmasına rağmen, tanısal yorumlar büyük ölçüde uzman deneyimine bağlıdır. Bu çalışmada, tiroid ultrason görüntülerinde güncel derin öğrenme mimarilerini sistematik olarak karşılaştırmak amacıyla standartlaştırılmış bir kıyaslama çerçevesi sunulmuştur. RegNetY-032, ResNet50, EfficientNet, Inception ve YOLOv8 sınıflandırma varyantlarını içeren toplam 15 model, 5000 görüntüden oluşan TN5000 veri kümesi üzerinde aynı ön işleme ve eğitim koşulları altında değerlendirilmiştir. Performans; Accuracy, Precision, Recall, F1-skoru ve AUROC metrikleriyle analiz edilmiştir. Sonuçlar, Inception-v3 modelinin en yüksek doğruluk (0,984) ve F1-skoruna (0,9263) ulaştığını, RegNetY-032'nin ise 0,941 AUROC değeriyle en güçlü ayırt edici performansı gösterdiğini ortaya koymuştur. Grad-CAM tabanlı açıklanabilirlik ve hata analizleriyle desteklenen çalışma, tekrarlanabilir ve klinik olarak yorumlanabilir bir değerlendirme çerçevesi sunmaktadır.

Keywords

• Derin Öğrenme
• Medikal Tanı
• Tiroid Nodülü
• Ultrason Görüntüleme
• RegNetY-032

Classification of Thyroid Nodules from Ultrasound Images Using Deep Learning Methods

Abstract

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.

Keywords

• Deep Learning
• Medical Diagnosis
• Thyroid Nodule
• Ultrasound Imaging
• RegNetY-032

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