In recent years, the incidence of thyroid nodules has shown an increasing trend year by year and has become one of the important diseases that endanger human health. Ultrasound medical images based on deep learning are widely used in clinical diagnos...
INTRODUCTION: We designed 5 convolutional neural network (CNN) models and ensemble models to differentiate malignant and benign thyroid nodules on CT, and compared the diagnostic performance of CNN models with that of radiologists.
International journal for numerical methods in biomedical engineering
Jan 18, 2021
Detecting malign cases from thyroid nodule examinations is crucial in healthcare particularly to improve the early detection of such cases. However, malign thyroid nodules can be extremely rare and is hard to find using the traditional rule based or ...
PURPOSE: To investigate whether a computer-aided diagnosis (CAD) program developed using the deep learning convolutional neural network (CNN) on neck US images can predict the BRAFV600E mutation in thyroid cancer.
PURPOSE: Clinically, the risk stratification of thyroid nodules is usually used to formulate the next treatment plan. The American College of Radiology (ACR) thyroid imaging reporting and data system (TI-RADS) is a type of medical standard widely use...
The classification of thyroid nodules using ultrasound (US) imaging is done using the Thyroid Imaging Reporting and Data System (TIRADS) guidelines that classify nodules based on visual and textural characteristics. These are composition, shape, size...
Radiologists very frequently encounter incidental findings related to the thyroid gland. Given increases in imaging use over the past several decades, thyroid incidentalomas are increasingly encountered in clinical practice, and it is important for r...