Table 2.

The effect of fine tuning and data augmentation techniques using the MVFCNN approach are depicted.

Network Architecture	Training Data	Training Strategy	Test Accuracy
MVFCNN	Unbalanced SEM	from scratch	55.50%
MVFCNN	Unbalanced SEM	fine tune MVFCNN	87.98%
MVFCNN	Balanced SEM	fine tune MVFCNN	90.97%
MVFCNN	Balanced and augmented SEM	fine tune MVFCNN	93.94%

The results show that fine tuning together with data augmentation achieves the best result. However, the effect of data augmentation is not significant.