Table 10 Accuracy comparison of the proposed algorithms with the other reports
From: Tumor segmentation via enhanced area growth algorithm for lung CT images
Refs. & Methods | Accuracy (average) | Size (number of CT scans in dataset) | Database |
|---|---|---|---|
Leader et al. 2003 [5] | 95.8% | 101 | GE Medical System, Milwaukee |
Kumar et al. [41] | 70.0% | 220 | LIDCa |
Shen et al. 2015 [43] | 92.6% | 233 | LIDC |
Wu et al. 2016 [44] | 87.6% | 60 | LIDC-IDRIb |
Uzelaltinbulat et al. 2017 [45] | 97.1% | 70 | LIDC |
Wang et al. 2018 [46] | 96.1% | 1018 | LIDC-IDRI |
Xu et al. 2019 [47] | 99.1% | 2460 | various databases |
Khehrah et al. 2020 [48] | 92.0% | 75 | LIDC |
Javan et al. 2021 [49] | 96.0% | 1000 | Local patients |
Primary algorithm (this study) | 84.7% | 170 | DIR-LCAc-NSCLCd-LIDC |
Enhanced algorithm (this study) | 92.1% | 170 | DIR-LCA-NSCLC-LIDC |