A Green AI Model Selection Strategy for Computer-Aided Mpox Detection

With the recent global surge in Mpox (formerly Monkeypox) cases, researchers have proposed deep learning technologies for early detection of the disease from skin lesion images. However, many of these researchers follow the current Red AI trend of seeking to improve the performance accuracies of classifiers with no consideration given to the efficiency and environmental-friendliness of their models. This paper proposes a Green AI model selection strategy based on a multi-criteria decision technique, incorporating computational time in identifying the optimal model for final deployment. We have experimented with end-to-end ResNet50, VGG19, and InceptionV3 networks and the transfer-learning of their pre-trained versions with SVMs. Using our proposed Green AI strategy, we have identified the optimal models based on efficiency and performance. The results have been assessed using expert-level validation. We demonstrate that our proposed method can select the best model. The outcomes of our model selection strategy are similar to experts' choices of the optimal model when presented with both model error and computation time. This paper's contributions are significant as they support the ongoing call for Green AI, especially within the healthcare sector.