Realistic Skin Image Data Generation Leveraging Conditional GAN and Classification Using Deep CNN

The recent surge in monkeypox (mpox) cases across various nations has escalated into a significant public health challenge, underscoring the imperative for timely detection and diagnosis. In light of this, our research focused on identifying the most efficient deep learning model tailored for mpox detection. Clinically, the progression of monkeypox is characterized by four distinct stages: macular, papular, vesicular, and pustular. In this study, we explore the viability of leveraging a Deep Convolutional Generative Adversarial Network (DCGAN) in conjunction with a conditional vector to strengthen the diagnostic precision of monkeypox. By utilising the remarkable capabilities of GANs, we generate synthetic images mirroring monkeypox skin lesions, thereby amplifying our limited dataset. This approach of utilizing a conditional DCGAN, when paired with stage-specific conditions, results in a significant boost in classification accuracy, elevating it from 0.7532 to 0.8734, complemented by precision 0.91, recall 0.878 F1-score 0.8936. These promising results underscore the advantages of deploying GANs for data augmentation in biomedical image classification tasks, when compared with well known classification models. These models assures the potential of deep learning-assisted diagnostics in dermatology. Especially in therapeutic contexts, where real data may be sparse, the addition of synthetic images can act as a valuable resource, paving the way for enhanced diagnostic instruments.