ArticlePeerJ. Computer science2025
Advancing thyroid diagnosis: integrating AI-driven CAD framework with numerical data and ultrasound images.
Article in PeerJ. Computer science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Automated segmentation and quantitative measurement of cervical nerves in ultrasound images using an SZJ-SEG-based deep learning framework.Quantitative imaging in medicine and surgery · 2026Article
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Abstract
This study proposes an advanced computer-aided diagnosis (CAD) framework for thyroid disease diagnosis that integrates numerical patient data and ultrasound images. The framework uses cutting edge technologies, including Vision Transformers (ViTs) and SHapley Additive exPlanations (SHAPs), to increase diagnostic accuracy, interpretability, and clinical applicability. The proposed CAD framework employs the sparse search algorithm (SSA) for optimized feature selection from numerical data and the tree-structured Parzen estimator for tuning the hyperparameters. ViTs are utilized for analyzing thyroid ultrasound images, whereas SHAP provides explainable AI insights into model predictions. Extensive experiments were conducted on two datasets: the thyroid disease patient dataset and the DDTI: Thyroid Ultrasound Images dataset. Performance was evaluated
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