Table 2.

Details of Performed of Our Developed Deep Learning Model

Sample size (patients) Sample ratio(patients)	230, 79.9% for training, 58, 20.1% for validation, total 288
	Favor: 148, 51.4%; poor:140, 48.6%
	Favor: 118, 51.3%; poor:112, 48.7% for training
	Favor: 30, 51.7%; poor: 28, 48.3% for validation
Model details	EfficientNetV2B0 CNN model with transfer learning (freeze all layers without top)
	RMSProp optimizer, ReLU activation
	Learning rate 1e-05, batch size 8, training epoch 26
	Batch Normalization and dropout for regularization
	ROI image resized to (100 × 100)
	Training accuracy: 89.6%, AUC 0.981 with 95% CI [0.969–0.993]
	Validation accuracy: 79.3%, AUC 0.802 with 95% CI [0.682–0.923]
Model performance (validation data)	Class	Precision	Recall	F1-score	Support
	Poor(0)	0.767	0.821	0.793	28
	Favor(1)	0.821	0.767	0.793	30
	Macro average	0.794	0.794	0.793	58

Abbreviations: CNN, convolutional neural network; RMSProp, root mean squared propagation; ReLU, rectified linear units; ROI, region of interest; AUC, area under the curve; CI, confidence interval.