Table 5 Comparison of classification methods for thoracic diseases. The classification methods, measurements, and best results in the review are shown in each column, respectively
From: Computer-aided detection in chest radiography based on artificial intelligence: a survey
| Â | Methods | Measurements | Best results |
|---|---|---|---|
Traditional machine learning methods | Maharanobis distance [70] | AUC | Lung nodules: 85% |
KNN [67] | Sensitivity | Lung nodules 4FP/image: 67% | |
ANN [71] | Sensitivity | Lung nodules 5.05FP/image: 70.1% | |
Sensitivity, accuracy Specificity, AUC | Lung nodules: sensitivity 5FP/image: 83.3% Tuberculosis: accuracy 82.8%, specificity 86.8%, sensitivity 78.8% Cardiomegaly: accuracy 76.5%, sensitivity 77.1%, AUC 79.2% Pleural effusion: AUC 80% Septum enlargement: AUC 88.2% | ||
Fisher linear discriminant [72] | Sensitivity | Lung nodules 4FP/image: 78.1% | |
Minimum distance [80] | Accuracy | Tuberculosis: 95.7% | |
Decision tree [81] | Accuracy | Tuberculosis: 94.9% | |
Bayesian classifier [88] | Sensitivity | Tuberculosis: 0.237 FP/image: 82.35% | |
Traditional machine learning methods + CNN | CNN transfer learning + SVM [75] | AUC | Right pleural effusion: 93% Cardiomegaly: 89% |
AlEXNET transfer learning + random forests [77] | Sensitivity, specificity | Lung nodules: 1.19FP/image: sensitivity 69.27%, specificity 96.02% | |
Deep learning methods | RESNET transfer learning [76] | Sensitivity Specificity | Lung nodules: sensitivity 92%, specificity 86% |
CNN transfer learning [90] | AUC, accuracy | Tuberculosis: 96.4%, 90.3% | |
CNN [101] | Sensitivity, accuracy, AUC, specificity | Cardiomegaly: 93%, 97%, 94%, 92% | |
GoogleNet CNN [106] | AUC | Cardiomegaly: 87.5%, pneumothorax: 86.1%, pleural effusion: 96.2%, pulmonary edema: 86.8% |