Table 2.

A comparative analysis of the relevant studies of monkeypox detection using deep learning methods.

Author/year	Purpose	Proposed methodology	Key parameters	Model
Ali et al., 202231	Monkeypox skin lesion detection	Utilizing deep learning models for detecting monkeypox skin lesions	F1-score	VGG-16, ResNet50, and InceptionV3 models
Situla and Sahahi, 202232	Monkeypox virus detection	Detection of monkeypox virus by transfer learning methods	Accuracy and F1-score	Xception, DenseNet
Ahsan et al., 202033	Detecting monkeypox disease	Image data collection and implementation of a deep learning-based model in detecting monkeypox disease	AUC	They propose and evaluate a VGG16 model with D curve
Sahin et al., 202228	Human monkeypox classification from skin lesion images	Human monkeypox classification from skin lesion images with deep pre-trained network	Accuracy and F1-score	GoogleNet, EfficientNetb0, NasnetMobile, ShuffleNet, MobileNetv2 models
Hossain et al., 202234	Lyme disease from skin lesion images	Convolutional neural networks with transfer learning to diagnose Lyme disease from skin lesion	AUC, sensitivity, accuracy and specificity	ResNet50
Philippe et al., 201935	Automated detection of erythema migrans	Automated detection of erythema migrans and other confounding skin lesions via deep learning	AUC and accuracy	Resnet50
Proposed method	Automated classification of monkeypox skin lesions	Automated classification of monkeypox skin lesions using CNNs and GWO optimization	Accuracy Precision Recall F1 Score AUC Score	CNNs and GWO