Table 13 Analysis metric for CNN, VGG16, Ensembled model.
From: Revolutionizing healthcare: a comparative insight into deep learning’s role in medical imaging
Factor | CNN | VGG16 | Ensembled model |
|---|---|---|---|
Architecture | Custom deep learning specially designed for spatial data such as MRI | Predefined, 16 layers, 3X3 filter, and max-pooling layer that helps to learn hierarchical features from the input MRI images | Combination of models, possibly including CNNs, VGG16, and other architectures. |
Performance metrics | F1-score = 96.5, Sensitivity = 96.5, Precision = 96.5 | F1-score = 96, Sensitivity = 97, Precision = 94.5 | F1-score = 98.5, Sensitivity = 98.7, Precision = 98.25 |
Computational complexity | High when dealing with complex MRI dataset | Model depth contribute to high computational complexity | Depend on the number of base model and ensembled method |
Training and inference time | Longer training time | Longer training time | Longer training time |
Interpretability | Difficult to understand the specific features or patterns in the images that lead to those predictions. | Challenging to interpret how it makes decisions based on MRI images | More interpretable and depend on the base model. |
Robustness | Robust due to hierarchical features of images | Robust due to ability to learn features from images | Potential for improved robustness |
