Table 13.

Analysis metric for CNN, VGG16, Ensembled model.

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