ThyroNet-XAI: An Explainable Multimodal Framework for Thyroid Cancer Analysis Using Clinical and Ultrasound Data

Thyroid cancer diagnosis requires accurate assessment of clinical characteristics and ultrasound imaging, yet existing artificial intelligence (AI) approaches often rely on a single data modality and provide limited interpretability. This paper presents ThyroNet-XAI, an explainable multimodal framework that integrates clinical tabular data and thyroid ultrasound images for automated thyroid cancer diagnosis and nodule analysis. The proposed framework employs SMOTE-ENN to address severe class imbalance in clinical data, followed by ANOVA-based feature selection to identify informative clinical attributes. A hybrid Random Forest–Deep Neural Network (RF-DNN) architecture is used for clinical classification, while an FCN–TransUNet architecture combines convolutional feature extraction with transformer-based global contextual learning for ultrasound nodule segmentation. To improve transparency, SHAP and LIME are incorporated for clinical predictions, while Grad-CAM provides visual explanations for image-based decisions. The framework is evaluated using the Kaggle thyroid clinical dataset, TN3K ultrasound dataset, and DDTI dataset for external validation. Experimental results reported in the study demonstrate 98% classification accuracy for the clinical branch, with the proposed hybrid clinical model achieving an F1-score of 0.7179 and ROC-AUC of 0.9803. For ultrasound analysis, the proposed segmentation framework achieves a Dice coefficient of 0.8974 and IoU of 0.8138, while multimodal imaging evaluation reports an AUC of 0.9942. These findings demonstrate the potential of combining multimodal learning, hybrid architectures, imbalance-aware preprocessing, and explainable AI to develop accurate and clinically interpretable thyroid cancer decision-support systems.