دورية أكاديمية
Explainable Automated TI-RADS Evaluation of Thyroid Nodules
| العنوان: | Explainable Automated TI-RADS Evaluation of Thyroid Nodules |
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| المؤلفون: | Alisa Kunapinun, Dittapong Songsaeng, Sittaya Buathong, Matthew N. Dailey, Chadaporn Keatmanee, Mongkol Ekpanyapong |
| المصدر: | Sensors, Vol 23, Iss 16, p 7289 (2023) |
| بيانات النشر: | MDPI AG, 2023. |
| سنة النشر: | 2023 |
| المجموعة: | LCC:Chemical technology |
| مصطلحات موضوعية: | thyroid nodule, TI-RADS, classification, deep learning, Grad-CAM, heatmap, Chemical technology, TP1-1185 |
| الوصف: | 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. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 23167289 1424-8220 |
| العلاقة: | https://www.mdpi.com/1424-8220/23/16/7289Test; https://doaj.org/toc/1424-8220Test |
| DOI: | 10.3390/s23167289 |
| الوصول الحر: | https://doaj.org/article/e19a168cd0d340b89b05d7ffab9cd061Test |
| رقم الانضمام: | edsdoj.19a168cd0d340b89b05d7ffab9cd061 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 23167289 14248220 |
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| DOI: | 10.3390/s23167289 |