Table 5.
Comparative performance analysis of the proposed ensemble-SDCNNa model with various state-of-the-art methods.
| Comparative methods | MCb | SZc | MC + SZ | ||||||||||||||
| F1 | APd | ARe | ACCf | AUCg | F1 | AP | AR | ACC | AUC | F1 | AP | AR | ACC | AUC | |||
| LBPh and SVMi,j [46] | 0.537 | 0.58 | 0.5 | 0.58 | 0.675 | 0.76 | 0.76 | 0.76 | 0.76 | 0.83 | 0.729 | 0.729 | 0.729 | 0.729 | 0.763 | ||
| HoGk and SVMi [47] | 0.797 | 0.796 | 0.798 | 0.797 | 0.863 | 0.85 | 0.85 | 0.85 | 0.85 | 0.90 | 0.822 | 0.823 | 0.821 | 0.821 | 0.882 | ||
| ShuffleNeti [43] | 0.747 | 0.771 | 0.727 | 0.748 | 0.84 | 0.875 | 0.876 | 0.873 | 0.873 | 0.937 | 0.884 | 0.885 | 0.883 | 0.884 | 0.936 | ||
| InceptionV3i [44] | 0.739 | 0.773 | 0.711 | 0.74 | 0.828 | 0.882 | 0.883 | 0.881 | 0.881 | 0.942 | 0.887 | 0.89 | 0.884 | 0.885 | 0.944 | ||
| MobileNetV2i [45] | 0.762 | 0.769 | 0.755 | 0.769 | 0.833 | 0.876 | 0.878 | 0.875 | 0.875 | 0.941 | 0.886 | 0.888 | 0.883 | 0.884 | 0.946 | ||
| Santosh et al [41] | —l | — | — | 0.79 | 0.88 | — | — | — | 0.86 | 0.93 | — | — | — | — | — | ||
| Hwang et al [17] | — | — | — | 0.674 | 0.884 | — | — | — | 0.837 | 0.926 | — | — | — | — | — | ||
| ResNet50i [29] | 0.788 | 0.796 | 0.78 | 0.79 | 0.886 | 0.877 | 0.877 | 0.877 | 0.876 | 0.94 | 0.88 | 0.881 | 0.878 | 0.879 | 0.921 | ||
| ResNet101i [29] | 0.8 | 0.821 | 0.782 | 0.798 | 0.895 | 0.864 | 0.865 | 0.862 | 0.861 | 0.934 | 0.859 | 0.862 | 0.857 | 0.858 | 0.923 | ||
| Alfadhli et al [14] | — | 0.81 | 0.79 | 0.791 | 0.89 | — | — | — | — | — | — | — | — | — | — | ||
| GoogLeNeti [20,21] | 0.834 | 0.851 | 0.818 | 0.834 | 0.902 | 0.852 | 0.853 | 0.851 | 0.851 | 0.921 | 0.843 | 0.846 | 0.84 | 0.84 | 0.914 | ||
| Lopes and Valiati [21] | — | — | — | 0.826 | 0.926 | — | — | — | 0.847 | 0.904 | — | — | — | — | — | ||
| Vajda et al [42] | — | — | — | 0.783 | 0.87 | — | — | — | — | — | — | — | — | — | — | ||
| Pasa et al [22] | — | — | — | 0.79 | 0.811 | — | — | — | 0.844 | 0.9 | — | — | — | 0.862 | 0.925 | ||
| Govindarajan and Swaminathan [15] | 0.876 | — | 0.877 | 0.878 | 0.94 | — | — | — | — | — | — | — | — | — | — | ||
| Proposed | 0.929 | 0.937 | 0.921 | 0.928 | 0.965 | 0.908 | 0.909 | 0.908 | 0.908 | 0.948 | 0.9 | 0.902 | 0.898 | 0.899 | 0.95 | ||
aSDCNN: shallow–deep CNN.
bMC: Montgomery County.
cSZ: Shenzhen.
dAP: average precision.
eAR: average recall.
fACC: accuracy.
gAUC: area under the curve.
hLBP: local binary pattern.
iWe evaluated the performance of these models using our selected data sets and experimental protocol.
jSVM: support vector machine.
kHoG: histogram of oriented gradients.
l—: not available. These results were not reported in some existing studies.