Table 5.

A comparison of our approach with previous deep learning methodologies for bacterial classification

Approach	Number of images	Augmentation	Data split	Loss	Accuracy	Precision	Recall	FScore	MCC
Proposed model 1	660	$✓$	7:2:1	0.2674	99.91	98.98	98.48	98.38	98.52
Proposed model 2	660	$✓$	7:2:1	0.0431	99.82	97.98	96.97	96.77	97.04
ResNet-50 [14]	660	$✓$	7:2:1	0.0155	99.72	–	95.45	94.34	–
MobileNetV2 [17]	660	$✓$	7:2:1	3.0262	95.04	–	18.18	11.64	–
VGG16 [21]	660	$✓$	7:2:1	3.5460	94.58	–	10.61	5.82	–