The tables below gives accuracy of each model for each magnification zoom presents in the dataset upto three decimal units. The values in brackets are F1 score
CNN models with FCN at the end
| Magnification/CNN Model -> | VGG-16 | VGG-19 | Xception | Resnet | Inception | Inception-Resnet-V3 |
|---|---|---|---|---|---|---|
| 40X | 0.802 (0.803) | 0.652 (0.685) | 0.831 (0.831) | 0.859 (0.858) | 0.853 (0.858) | 0.818 (0.813) |
| 100X | 0.867 (0.877) | 0.709 (0.708) | 0.786 (0.794) | 0.911 (0.917) | 0.834 (0.827) | 0.845 (0.837) |
| 200X | 0.841 (0.839) | 0.749 (0.756) | 0.812 (0.813) | 0.857 (0.853) | 0.799 (0.806) | 0.854 (0.859) |
| 400X | 0.871 (0.869) | 0.799 (0.799) | 0.761 (0.758) | 0.903 (0.907) | 0.799 (0.796) | 0.842 (0.844) |
CV score on Logistic Regression Model trained on features extracted from CNN models
| Magnification/CNN Model -> | VGG-16 | VGG-19 | Xception | Resnet | Inception | Inception-Resnet-V3 |
|---|---|---|---|---|---|---|
| 40X | 0.685 (0.675) | 0.565 (0.547) | 0.858 (0.856) | 0.908 (0.906) | 0.839 (0.836) | 0.854 (0.850) |
| 100X | 0.732 (0.725) | 0.633 (0.623) | 0.840 (0.837) | 0.902 (0.900) | 0.826 (0.822) | 0.863 (0.862) |
| 200X | 0.864 (0.862) | 0.725 (0.718) | 0.940 (0.954) | 0.959 (0.958) | 0.919 (0.917) | 0.961 (0.960) |
| 400X | 0.952 (0.952) | 0.876 (0.874) | 0.982 (0.982) | 0.983 (0.983) | 0.983 (0.983) | 0.982 (0.982) |
CV score on Linear Support Vector Machine Model trained on features extracted from CNN models
| Magnification/CNN Model -> | VGG-16 | VGG-19 | Xception | Resnet | Inception | Inception-Resnet-V3 |
|---|---|---|---|---|---|---|
| 40X | 0.644 (0.640) | 0.543 (0.530) | 0.857 (0.856) | 0.905 (0.905) | 0.855 (0.853) | 0.851 (0.849) |
| 100X | 0.711 (0.704) | 0.603 (0.595) | 0.830 (0.829) | 0.895 (0.894) | 0.826 (0.822) | 0.864 (0.863) |
| 200X | 0.848 (0.847) | 0.700 (0.693) | 0.943 (0.942) | 0.961 (0.961) | 0.916 (0.916) | 0.958 (0.958) |
| 400X | 0.950 (0.949) | 0.868 (0.867) | 0.983 (0.983) | 0.983 (0.983) | 0.983 (0.983) | 0.980 (0.980) |