An Adaptive Generative 3D VNet Model for Enhanced Monkeypox Lesion Classification Using Deep Learning and Augmented Image Fusion.

As monkeypox is spreading rapidly, the incidence of monkeypox has been increasing in recent times. Therefore, it is very important to detect and diagnose this disease to get effective treatment planning. The prominent aim of this paper is to design an effective monkeypox detection and classification model by utilizing deep learning and classification models. In this study, a novel Adaptive Generative 3D VNet model is presented to effectively classify the monkeypox lesions. Different data augmentation approaches, deep learning, and adaptive fusion are integrated into the proposed model to attain better results in disease classification. The major objective of the proposed model is to mitigate the challenges of limited labeled data by generating synthetic augmented images and combining them with real images for robust classification. The two major components of the proposed system are the Adaptive Generative Network and the 3D VNet. Additional training models are generated by the adaptive generative network through augmentation approaches including cropping, rotation, and flipping, thereby increasing the diversity of the dataset. The 3D VNet processes these images in a volumetric manner to capture spatial relationships within the lesions, improving classification accuracy. The fusion layer then adaptively combines the predictions from the real and augmented data to optimize the overall effectiveness of a model. Key performance metrics including accuracy, precision, sensitivity, specificity, Jaccard Index, Hausdorff distance, and Dice Similarity Coefficient are used to compute the effectiveness of a model. The findings show that the Adaptive Generative 3D VNet model outperforms traditional 2D models by significantly improving the classification accuracy and robustness, especially in the presence of limited labeled data. Therefore, the simulation results demonstrate that the proposed model achieves high accuracy and precision of 98.8% and 98.5%, respectively based on the Monkeypox Skin Lesion Dataset.