Hyper-Tuned CNN Using EVO Technique for Efficient Biomedical Image Classification

Table 8

Performance of evaluation of traditional ANN-based strategies, DNN based strategies, and the proposed hyperparameter tuned CNN based on EVO for the Glioma dataset.

Models	Class	Accuracy	Precision	Recall	Specificity	F1-score	MCR	FDR	TNR
MLP	Glioma tumour	0.8372	0.6731	0.7000	0.8840	0.6863	0.1628	0.3269	0.3
	Meningioma tumour	0.8144	0.6612	0.7207	0.8520	0.6897	0.1856	0.3388	0.2793
	No tumour	0.8663	0.7609	0.7000	0.9239	0.7292	0.1337	0.2391	0.3
	Pituitary tumour	0.9049	0.7463	0.7143	0.9467	0.7299	0.0951	0.2537	0.2857
ELM	Glioma tumour	0.8878	0.7407	0.8333	0.9054	0.7843	0.1122	0.2593	0.1667
	Meningioma tumour	0.8817	0.7627	0.8333	0.9004	0.7965	0.1183	0.2373	0.1667
	No tumour	0.9302	0.8989	0.8163	0.9689	0.8556	0.0698	0.1011	0.1837
	Pituitary tumour	0.9308	0.8000	0.8116	0.9564	0.8058	0.0692	0.2	0.1884
DNN	Glioma tumour	0.9412	0.8700	0.8969	0.9558	0.8832	0.0588	0.13	0.1031
	Meningioma tumour	0.9487	0.9091	0.9091	0.9643	0.9091	0.0513	0.0909	0.0909
	No tumour	0.9523	0.9000	0.9000	0.9687	0.9000	0.0477	0.1	0.1
	Pituitary tumour	0.9512	0.8696	0.8571	0.9718	0.8633	0.0488	0.1304	0.1429
CNN	Glioma tumour	0.9820	0.9574	0.9677	0.9864	0.9626	0.018	0.0426	0.0323
	Meningioma tumour	0.9747	0.9402	0.9735	0.9752	0.9565	0.0253	0.0598	0.0265
	No tumour	0.9844	0.9890	0.9474	0.9966	0.9677	0.0156	0.011	0.0526
	Pituitary tumour	0.9746	0.9333	0.9333	0.9843	0.9333	0.0254	0.0667	0.0667
Hyperparameter tuned CNN	Glioma tumour	0.9847	0.9691	0.9691	0.9898	0.9691	0.0153	0.0309	0.0309
	Meningioma tumour	0.9847	0.9735	0.9735	0.9892	0.9735	0.0153	0.0265	0.0265
	No tumour	0.9923	0.9901	0.9804	0.9965	0.9852	0.0077	0.0099	0.0196
	Pituitary tumour	0.9873	0.9722	0.9589	0.9938	0.9655	0.0127	0.0278	0.0411