Lightweight Residual Network for The Classification of Thyroid Nodules
This work addresses the challenge of improving diagnostic accuracy for thyroid nodules in medical imaging, but it appears incremental as it adapts an existing method to a specific domain.
The paper tackled the problem of automatically classifying thyroid nodules in ultrasound images to aid diagnosis, achieving a classification accuracy of 95%.
Ultrasound is a useful technique for diagnosing thyroid nodules. Benign and malignant nodules that automatically discriminate in the ultrasound pictures can provide diagnostic recommendations or, improve diagnostic accuracy in the absence of specialists. The main issue here is how to collect suitable features for this particular task. We suggest here a technique for extracting features from ultrasound pictures based on the Residual U-net. We attempt to introduce significant semantic characteristics to the classification. Our model gained 95% classification accuracy.