Table 7.

Comparison results of the state-of-art models for automated medical diagnosis of malaria

Reference	Architecture	Accuracy	Recall	Precision	F1-Score
Liang [19]	Transfer Learning	0.9199	0.8900	0.9512	0.9024
Liang [19]	Own CNN	0.9737	0.9775	0.9699	0.9736
Rajaraman [32]	Own CNN	0.9400	0.9310	0.9512	0.9410
Rajaraman [32]	ResNet50	0.9570	0.9450	0.9690	0.9570
Rahman [31]	Own CNN	0.9629	0.9234	0.9804	0.9495
Rahman [31]	VGG16	0.9777	0.9720	0.9719	0.9709
Shah [39]	Own CNN	0.9477	0.9526	0.9437	0.9481
Quan [30] [11]	DenseNet121	0.9094	0.9251	0.8960	0.9103
Quan [30] [5]	DPN92	0.8788	0.8681	0.8892	0.8785
Quan [30]	ADCN	0.9747	0.9520	0.9350	0.9434
Yang [59]	VGG19	0.9372	0.8731	0.5299	0.6595
Yang [59]	AlexNet	0.9633	0.8215	0.7023	0.7573
Yang [59]	Own CNN	0.9726	0.8273	0.7898	0.8081
Proposed	EfficientNetB0	0.9829	0.9882	0.9774	0.9828