Table 9 Comparison with state-of-the-art methods
From: A multilevel features selection framework for skin lesion classification
Ref | Year | Dataset | Method | OA (%) |
|---|---|---|---|---|
[65] | 2016 | \(PH^{2}\) | ABCD rule | 90.00 |
[66] | 2016 | \(PH^{2}\) | wavelet transform with morphological operations | 93.87 |
[15] | 2017 | \(PH^{2}\) | multistage fully convolutional network | 94.24 |
[67] | 2017 | \(PH^{2}\) | color and texture features | 96.00 |
[68] | 2018 | ISBI-2017 | regularised discriminant learning | 83.20 |
[13] | 2018 | ISBI-2017 | fully convolutional residual networks & lesion index calculation unit | 85.70 |
[69] | 2018 | ISBI-2017 | Ensemble Of Deep Neural Networks | 84.8% |
[18] | 2018 | ISIC-MSK | probabilistic distribution and best features selection | 97.20 |
Proposed | 2019 | ISBI-2017 | ECNCA | 95.90 |
Proposed | 2019 | ISIC-UDA | ECNCA | 97.10 |
Proposed | 2019 | ISIC-MSK | ECNCA | 99.20 |
Proposed | 2019 | \(PH^{2}\) | ECNCA | 98.80 |