Investigation of optimal convolutional neural network conditions for thyroid ultrasound image analysis.

ABSTRACT: Neural network models have been used to analyze thyroid ultrasound (US) images and stratify malignancy risk of the thyroid nodules. We investigated the optimal neural network condition for thyroid US image analysis. We compared scratch and transfer learning models, performed stress tests in 10% increments, and compared the performance of three threshold values. All validation results indicated superiority of the transfer learning model over the scratch model. Stress test indicated that training the algorithm using 3902 images (70%) resulted in a performance which was similar to the full dataset (5575). Threshold 0.3 yielded high sensitivity (1% false negative) and low specificity (72% false positive), while 0.7 gave low sensitivity (22% false negative) and high specificity (23% false posit

SUBMITTER: Lee JH 

PROVIDER: S-EPMC9873643 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature