Table 2 Comparison of accuracy in pneumoconiosis diagnosis with different algorithms
From: Pneumoconiosis computer aided diagnosis system based on X-rays and deep learning
Machine learning classifiers | Accuracy (%) |
|---|---|
OC-SVM-RBF with raw CXR | 73.17 |
Isolation Forest with raw CXR | 68.29 |
Autoencoder and feed-forward NN with raw CXR | 68.29 |
MLP | 71.77 |
SVM-RBF with raw CXR | 68.29 |
Autoencoder and SVM-RBF with raw CXR | 73.17 |
NN | 83.00 |
Linear- SVM | 74.80 |
KNN | 69.30 |
Ridge Classifier | 76.90 |
Random Forest | 70.80 |
Res-Net (Our method) | 92.46 |