Explainable Automated TI-RADS Evaluation of Thyroid Nodules
Issued Date
2023-08-01
Resource Type
ISSN
14248220
Scopus ID
2-s2.0-85168732578
Pubmed ID
37631825
Journal Title
Sensors
Volume
23
Issue
16
Rights Holder(s)
SCOPUS
Bibliographic Citation
Sensors Vol.23 No.16 (2023)
Suggested Citation
Kunapinun A., Songsaeng D., Buathong S., Dailey M.N., Keatmanee C., Ekpanyapong M. Explainable Automated TI-RADS Evaluation of Thyroid Nodules. Sensors Vol.23 No.16 (2023). doi:10.3390/s23167289 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/89155
Title
Explainable Automated TI-RADS Evaluation of Thyroid Nodules
Other Contributor(s)
Abstract
A thyroid nodule, a common abnormal growth within the thyroid gland, is often identified through ultrasound imaging of the neck. These growths may be solid- or fluid-filled, and their treatment is influenced by factors such as size and location. The Thyroid Imaging Reporting and Data System (TI-RADS) is a classification method that categorizes thyroid nodules into risk levels based on features such as size, echogenicity, margin, shape, and calcification. It guides clinicians in deciding whether a biopsy or other further evaluation is needed. Machine learning (ML) can complement TI-RADS classification, thereby improving the detection of malignant tumors. When combined with expert rules (TI-RADS) and explanations, ML models may uncover elements that TI-RADS misses, especially when TI-RADS training data are scarce. In this paper, we present an automated system for classifying thyroid nodules according to TI-RADS and assessing malignancy effectively. We use ResNet-101 and DenseNet-201 models to classify thyroid nodules according to TI-RADS and malignancy. By analyzing the models’ last layer using the Grad-CAM algorithm, we demonstrate that these models can identify risk areas and detect nodule features relevant to the TI-RADS score. By integrating Grad-CAM results with feature probability calculations, we provide a precise heat map, visualizing specific features within the nodule and potentially assisting doctors in their assessments. Our experiments show that the utilization of ResNet-101 and DenseNet-201 models, in conjunction with Grad-CAM visualization analysis, improves TI-RADS classification accuracy by up to 10%. This enhancement, achieved through iterative analysis and re-training, underscores the potential of machine learning in advancing thyroid nodule diagnosis, offering a promising direction for further exploration and clinical application.