Table 8 A comparison between the proposed methodology (PM) and the state-of-the-art techniques.
Authors/ref. | Methodology | Modality | Accuracy |
|---|---|---|---|
S. Gudadhe et al.13 | GLCM, DC, DW with Machine Learning | CT-Scan | 87.22% |
S. M. Vijithananda et al.14 | GLCM with Machine Learning | MRI | 90.41% |
J. Ker et al.15 | Cellular Features with Deep Learning | HI | 91% |
M. Woźniak et al.16 | Statistical Features, Correlation Learning | CT-Scan | 96% |
B. Omarov et al.17 | Deep Features and Internet of Medical Things | CT-Scan | 79% |
N. M. Dawood et al.18 | Deep Features with Deep Neural Network | CT-Scan | 97.6% |
F. Fahmi et al.19 | Zoning Features, Learning Vector Quantization | CT-Scan | 85% |
B. A. Mohammed et al.20 | Deep Features, Deep Learning | MRI | 96% |
M. Devi et al.21 | GLCM, GLRLM, Machine Learning | MRI | 95% |
Proposed methodology | Optimized Statistical Features + ABTFCS + MLP | CT-Scan | 97.83% |
Proposed methodology validation | Optimized Statistical Features + ABTFCS + MLP | CT-Scan | 96.33% |
