Fahima akter nila^1,, Rafsana Ferdouse²

The prevalence of thyroid nodules in general population is very high, but only a small percentage of the nodules become malignant; thus, a proper risk stratification is necessary to prevent unnecessary biopsies and prompt cancer diagnosis. The most common diagnostic modality used to examine the thyroid is ultrasound imaging, but it is a subjective form of interpretation that is prone to inter-observer variability despite standardized systems like ACR TI-RADS. Despite convolutional neural networks (CNNs) achieving encouraging outcomes in the classification of thyroid ultrasounds, most of the current models do not incorporate hybrid multi-scale feature extraction and effective attention in order to improve the discriminative effectiveness. The paper presents a hybrid CNN model called ThyroUS-Net to achieve superior/significantly improved classification of thyroid nodule malignancy. The model combines shallow and deep convolutional networks to extract local texture and global morphological information, and SE blocks are used to recalibrate channels channelwise to highlight the diagnostically meaningful information. The network was tested on the Thyroid Digital Image Database (that is publicly accessible: 428 ultrasound images: 357 benign, 71 malignant). Untrained data augmentation, standard preprocessing, and stratified train-validation-test splitting were used to guarantee sound evaluation. According to the results of the experiment, ThyroUS-Net is superior to the baseline models, such as simple CNN, VGG-16, or ResNet-50, with accuracy, precision, recall, and F1 score of 95, 98.4, 97.1, and AUC of 0.97, respectively. The ablation analysis proved that both the hybrid backbone and SE attention module were significant in boosting performance. Such results indicate that ThyroUS-Net has better diagnostic consistency and better malignancy discrimination, which can be used as a clinical assistance system. The future research will involve multi-center validation, TI-RADS scoring integration, and explainable AI techniques to promote clinical adoption.

Thyroid nodule classification, Ultrasound imaging, Hybrid CNN, Squeeze-Excitation network, Deep learning in healthcare, Computer-aided diagnosis