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Proposed methods results for MNIST dataset_
| Method | Saeedan et al. (2018) | Lee et al. (2016) | FTM1 | FTM2 | FTM3 |
|---|---|---|---|---|---|
| Accuracy (%) | 98.80 | 98.72 | 99.95 | 99.84 | 99.96 |
Accuracy results for CIFAR10 dataset classification_
| Method | Saeedan et al. (2018) | Lee et al. (2016) | RFT1 | RFT2 | RFT3 |
|---|---|---|---|---|---|
| Accuracy (%) | 72.59 | 72.4 | 73.88 | 73.82 | 73.76 |
Performance of proposed methods_
| Images | Metrics | Saeedan et al. (2018) | Lee et al. (2016) | FTM1 | FTM2 | FTM3 |
|---|---|---|---|---|---|---|
| Lena | SNR dB | 23.28 | 24.01 | 24.1754 | 24.1603 | 24.16785 |
| SSIM | 0.7882 | 0.7893 | 0.79396 | 0.77886 | 0.78641 | |
| Correlation | 0.9822 | 0.9833 | 0.9846 | 0.9695 | 0.97705 | |
| Cameraman | SNR | 20.14 | 21.87 | 20.2935 | 20.2784 | 20.28595 |
| SSIM | 0.7854 | 0.7867 | 0.7891 | 0.774 | 0.78155 | |
| Correlation | 0.9814 | 0.9843 | 0.9952 | 0.9801 | 0.98765 | |
| Barbara | SNR | 23.69 | 22.13 | 27.336 | 27.3209 | 27.32845 |
| SSIM | 0.7041 | 0.7065 | 0.7092 | 0.6941 | 0.70165 | |
| Correlation | 0.9622 | 0.9648 | 0.9612 | 0.9461 | 0.95365 | |
| Test image | SNR | 28.98 | 28.78 | 29.38 | 29.3649 | 29.37245 |
| SSIM | 0.7832 | 0.7841 | 0.7852 | 0.7701 | 0.77765 | |
| Correlation | 0.9913 | 0.9922 | 0.9965 | 0.9814 | 0.98895 |
Confusion matrix for (FTM1) method for MNIST Classification_
| Target class | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Class 0 | Class 1 | Class 2 | Class 3 | Class 4 | Class 5 | Class 6 | Class 7 | Class 8 | Class 9 | Sensitivity | |
| Output class | |||||||||||
| Class 0 | 250 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 100 |
| Class 1 | 0 | 250 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 100 |
| 10% | % | ||||||||||
| Class 2 | 0 | 0 | 250 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 100 |
| 10% | % | ||||||||||
| Class 3 | 0 | 0 | 0 | 250 | 0 | 0 | 0 | 0 | 0 | 0 | 100 |
| 10% | % | ||||||||||
| Class 4 | 0 | 0 | 0 | 0 | 250 | 0 | 0 | 0 | 0 | 0 | 100 |
| 10% | % | ||||||||||
| Class 5 | 0 | 0 | 0 | 0 | 0 | 250 | 0 | 0 | 0 | 0 | 100 |
| 10% | % | ||||||||||
| Class 6 | 0 | 0 | 0 | 0 | 0 | 0 | 249 | 0 | 0 | 0 | 100 |
| 10% | % | ||||||||||
| Class 7 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 250 | 0 | 0 | 100 |
| 10% | % | ||||||||||
| Class 8 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 250 | 0 | 99.9 |
| 10% | 6% | ||||||||||
| Class 9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 250 | 100 |
| 10% | % | ||||||||||
| 100 | 100 | 100 | 100 | 100 | 100 | 100 | 99.9 | 100 | 100 | 100 | |
| % | % | % | % | % | % | % | 6% | % | % | % | |
Confusion matrix for (FTM1) for CIFAR_10 classification_
| Target class | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| airplane | auto mobi | bird | cat | deer | dog | frog | hour | ship | truc | Sens | |
| Output class | |||||||||||
| airpla | 796 | 29 | 68 | 32 | 25 | 13 | 10 | 22 | 57 | 3 | 73 |
| autom | 10 | 829 | 6 | 6 | 4 | 2 | 3 | 4 | 15 | 7 | 86 |
| bird | 44 | 10 | 599 | 62 | 69 | 42 | 37 | 32 | 13 | 8 | 66 |
| cat | 18 | 3 | 61 | 533 | 36 | 16 | 57 | 43 | 10 | 9 | 56 |
| deer | 24 | 3 | 75 | 60 | 708 | 38 | 26 | 55 | 5 | 1 | 71 |
| dog | 4 | 4 | 109 | 201 | 55 | 69 | 22 | 97 | 4 | 3 | 58 |
| frog | 9 | 9 | 46 | 66 | 54 | 13 | 83 | 5 | 7 | 1 | 78 |
| hours | 7 | 1 | 16 | 12 | 38 | 25 | 3 | 72 | 2 | 4 | 86 |
| ship | 48 | 36 | 9 | 14 | 9 | 6 | 4 | 6 | 86 | 2 | 82 |
| truck | 38 | 75 | 8 | 13 | 4 | 6 | 2 | 8 | 18 | 80 | 83 |
| specifi | 79.8 | 82.3 | 59.8 | 53.3 | 71.6 | 69 | 83 | 73 | 86 | 82 | 74 |