Ultrasound Images Classification of Thyroid Cancer using Deep Transfer Learning
Yassine Habchi, Hamza Kheddar, Yassine Himeur · 2024
The application of computer-aided diagnosis (CAD) systems in ultrasound (US) image analysis using maching learning (ML) has been widely explored. Recently, deep learning (DL) has played a pivotal role in advancing this research. This paper proposes an automated method for thyroid cancer (TC) image classification, utilizing transfer learning (TL) with VGG16, a well-established pre-trained neural network. The approach involves preprocessing, data augmentation, and classification using TL. The results are compared to convolutional neural networks (CNNs) for distinguishing between benign and malignant thyroid nodules (TNs). Model performance is evaluated using accuracy, precision, sensitivity, specificity, and F1-score metrics. The promising results highlight the effectiveness of TL in US image analysis. The best results, achieved using the TL algorithm, include an accuracy of 0.9878, sensitivity of 0.9705, specificity of 0.9854, and an F1-score of 0.9866. These values represent significant improvements over the baseline models, and the study is conducted using the DDTI dataset for TC, distinguishing between benign and malignant cases.