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Table 6 Performance comparison of several existing computer aided detection systems which used JSRT Database

From: The detection of lung cancer using massive artificial neural network based on soft tissue technique

Author Sensitivity FPs/image Methodology Classifier Database
Wei et al. [48] 80% (123/154) 5.4 (1333/247) (less accuracy due to high FPs) Forward stepwise selection Fisher linear discriminant All abnormal as well as normal image inside JSRT(247)
Coppini et al. [49] 60% (93/154) 4.3 (662/154) Neural network filter Fisher linear discriminant All nodule image in JSRT(154)
Schilham et al. [13] 51% (79/154)
67% (103/154)
2 (308/154) 4 (616/154) Image filtering Based on Regression Fisher linear discriminant All nodule image in JSRT(154)
Hardie et al. [50] 80% (112/140)
63% (88/140)
5.0 (700/140)
2 (280/140)
Active shape model and new weighted multi- scale conver gence-index Fisher linear discriminant Nodule image in JSRT(140)
Chen et al. [31] 71% (100/140) 2 (466/233) Computer aided detection using neural filter Support Vector
Machine (SVM)
Nodule and Normal image in JSRT(233)
Proposed MANN based soft tissue technique 72.85% (102/140) 1 (233/233) MANN for rib suppression Support Vector
Machine (SVM)
Nodule and normal image in JSRT(233)