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Table 3 Tuberculosis detection. The datasets, manifestations, assessment measures and results are shown in each column, respectively

From: Computer-aided detection in chest radiography based on artificial intelligence: a survey

Study Datasets Manifestations Assessment measures Results
Rohmah et al. [80] Custom (120) Tuberculosis Accuracy, false accept rate, false rejection rate 95.7%, 3.33%, 6.67%
Tan et al. [81] Custom (95) Tuberculosis Accuracy, sensitivity, specificity, AUC, precision 92.9%, 91%, 95.4%, 92.8%, 94.9%
Noor et al. [82] TPR (100) Tuberculosis Accuracy 94%
Song et al. [87] Custom (200) Focal opacities Accuracy  
Shen et al. [88] Custom (243) Cavities True positive rate (or sensitivity) under FP/image 0.237 FP/image: 82.35%
Xu et al. [89] Custom Cavities Densitivity, specificity, and accuracy E-Group: 78.8%, 86.8%, 82.8%; D-Group: 69.4%, 81.6%, 75.5%
Hwang et al. [90] KIT, MC, Shenzhen Tuberculosis AUC, accuracy, positive precision, negative precision 96.4%, 90.3%, 95.3%, 97.4%
Lakhani et al. [91] Shenzhen Tuberculosis AUC, sensitivity, and specificity 99%, 97.3%, 100%