. 2026 Mar 21;26:148. doi: 10.1186/s12911-026-03451-y

Table 6.

Performance metrics of the Image Analysis → Red Blood Cell Indices analysis pipeline for β-thalassemia screening. VGG-16–based image analysis was applied first, followed by MLP analysis for image-negative cases

Step	Model	Sensitivity (%)	Specificity (%)	Classification Accuracy (%)
First step	Image Analysis (VGG-16)	88.2% (63.6–98.5%)	64.3% (44.1–81.4%)	73.3% (58.1–85.4%)
Second step	Red Blood Cell Indices Analysis (MLP)	100.0% (15.8–100.0%)	94.4% (72.7–99.9%)	95% (75.1–99.9%)
Overall Pipeline		100.0% (80.5–100.0%)	64.3% (44.1–81.4)	77.8% (62.9–88.8%)

Step

Model

Sensitivity (%)

Specificity (%)

Classification Accuracy (%)

First step

Image Analysis (VGG-16)

88.2%

(63.6–98.5%)

64.3%

(44.1–81.4%)

73.3%

(58.1–85.4%)

Second step

Red Blood Cell Indices Analysis (MLP)

100.0%

(15.8–100.0%)

94.4%

(72.7–99.9%)

95%

(75.1–99.9%)

Overall Pipeline

100.0%

(80.5–100.0%)

64.3%

(44.1–81.4)

77.8%

(62.9–88.8%)