Image Classification on Malaria
97.72AccuracyCNN
Evaluation results
| Method | Links | ||
|---|---|---|---|
| CNNReference=[10], Explainable AI=No2025.12 | 97.72 | — | |
| Attentive Dense Circular Net (ADCN) with attention and residualReference=[9], Explainable AI=No2025.12 | 97.47 | — | |
| CNNReference=[8], Explainable AI=No2025.12 | 97.37 | — | |
| CNNReference=[5], Explainable AI=No2025.12 | 96.82 | — | |
| Custom CNNExplainable AI=Yes (SHAP, LIME, Saliency maps)2025.12 | 96 | — | |
| Classical image processing (bilateral filtering + adaptive thresholding + morphological operations)Reference=[7], Explainable AI=No2025.12 | 91 | — | |
| BeerLaNetClassification Model=ResNet182025.10 | 48.66 | 90.33 | |
| VahadaneClassification Model=ResNet182025.10 | 38.81 | 81.04 | |
| StainGANClassification Model=ResNet182025.10 | 31.46 | 70.94 | |
| MacenkoClassification Model=ResNet182025.10 | 29.59 | 73.54 | |
| ReinhardClassification Model=ResNet182025.10 | 29.34 | 62.3 | |
| BaselineClassification Model=ResNet182025.10 | 21.32 | 45.59 | |
| LStainNormClassification Model=ResNet182025.10 | 21.17 | 61.82 |