Table 2.

Summary of machine learning classifiers applied in medical imaging for diagnostic and risk stratification tasks

Classifier	Data/Modality	Task	Sample size/Dataset	Key performance	References
RBF-SVM	Clinical+Imaging	Distinguish benign vs malignant	1,232 nodules from 724 patients	ML model outperformed human experts; exact metrics in original paper	Zhang et al., 2023b
CNN	Ultrasound images	Thyroid nodule detection	21,532 images from 5,842 patients	AUROC: 98.51%; sensitivity: 93.5% (R-CNN)	Xi et al., 2022
CNN	Ultrasound, radiologist comparison	Malignancy risk stratification	Not specified in snippet	Sensitivity: ~81.8%, specificity: ~86.1%, accuracy: ~85.1%	Kim et al., 2025
CBIR	MRI images (eye/orbit)	Diagnostic accuracy enhancement	48 cases interpreted by 36 radiologists	Accuracy improved from 55.9% to 70.6% with CBIR alone; further to 83.3% combined	Rumberger et al., 2025
RBM	Ultrasound features	Unsupervised feature learning	Various features across studies	RBM listed among feature extraction methods	(Wang et al., 2024