Table 10 Comparison of key metrics for... | Emerald Publishing

Table 10

Comparison of key metrics for Mpox detection models

Authors	Methods	Performance	CLAHE	L-CNN	Bayes opt
Kundu, Siddiqi, and Rahman (2022)	ViT	P_r: 84.82; R_e: 80.33; Fs: 82.73	×	×	×
Bala et al. (2023)	MonkeyNet	P_r: 79.48; R_e: 77.85; Fs: 78.56	×	×	×
Pramanik, Banerjee, Efimenko, Kaplun, and Sarkar (2023)	CNN with beta function	P_r: 88.91; R_e: 96.28	×	×	×
Shetty et al. (2022)	ML and CNN	P_r: 86; R_e: 85	×	×	×
Raha et al. (2024)	MobileNetV2	P_r: 90; R_e: 90; Fs: 93.39	×	×	×
Proposed	LDSCNN-ABBLM	MCSI: P_r: 75; R_e: 91; Fs: 82	✓	✓	✓
		MSLD v2: P_r:95; R_e:100; Fs:98

Authors	Methods	Performance	CLAHE	L-CNN	Bayes opt
Kundu, Siddiqi, and Rahman (2022)	ViT	P_r: 84.82; R_e: 80.33; Fs: 82.73	×	×	×
Bala et al. (2023)	MonkeyNet	P_r: 79.48; R_e: 77.85; Fs: 78.56	×	×	×
Pramanik, Banerjee, Efimenko, Kaplun, and Sarkar (2023)	CNN with beta function	P_r: 88.91; R_e: 96.28	×	×	×
Shetty et al. (2022)	ML and CNN	P_r: 86; R_e: 85	×	×	×
Raha et al. (2024)	MobileNetV2	P_r: 90; R_e: 90; Fs: 93.39	×	×	×
Proposed	LDSCNN-ABBLM	MCSI: P_r: 75; R_e: 91; Fs: 82	✓	✓	✓
		MSLD v2: P_r:95; R_e:100; Fs:98

Note(s): P_r–Precision, R_e–Recall, Fs–F1 − Score