Thyroid Nodule Benignty Prediction by Deep Feature Extraction
- Resource Type
- Authors
- Xiaomeng Dong; Theodore Trafalis; Timothy W. Deyer; Yan Fang; Xueyan Mei; Jingyi Zeng
- Source
- BIBE
- Subject
- Thyroid nodules
medicine.medical_specialty
Local binary patterns
business.industry
Thyroid
Feature extraction
Nodule (medicine)
02 engineering and technology
medicine.disease
Electronic mail
030218 nuclear medicine & medical imaging
03 medical and health sciences
0302 clinical medicine
Histogram of oriented gradients
medicine.anatomical_structure
0202 electrical engineering, electronic engineering, information engineering
medicine
Medical imaging
020201 artificial intelligence & image processing
Radiology
medicine.symptom
business
- Language
Thyroid nodules are a common pathology which are fortunately usually benign. However, current image characterization is limited in accurately differentiating benign from malignant nodules. Consequently, a percutaneous biopsy is often necessary to determine if a nodule is benign or malignant. We hypothesized that deep learning in conjunction with professional image characterization could improve nodule characterization and reduce benign biopsies. We extracted our features using convolutional auto-encoders, local binary patterns as well as histogram of oriented gradients descriptors in association with medical professional thyroid image characterization. The experiment showed the classifiers using these features can improve negative predictive value of thyroid nodule evaluation using ultrasound.