PoxNetX: Viral Pox skin lesion dataset

This dataset provides a curated, multi-source collection of skin lesion images spanning five clinically significant classes: Monkeypox, Chickenpox, Cowpox, Measles, and Normal (healthy) skin. These conditions share overlapping dermatological features, particularly at early lesion stages, making automated differential diagnosis both medically critical and technically challenging. The dataset was assembled as training data for hybrid convolutional neural network (CNN) architectures developed under the PoxNetX research framework at the Department of Information and Communication Technology, Comilla University, Bangladesh. Raw images were aggregated from 14 publicly available source dataset published between 2022 and 2025, hosted across Kaggle, Mendeley Data, and Roboflow repositories. Each source was selected on the basis of image quality, label accuracy, and license compatibility. After aggregation and quality filtering, the curated raw collection comprises 1,875 images distributed as follows: Monkeypox (556), Chickenpox (506), Measles (150), Cowpox (117), and Normal skin (547). Cowpox and Measles are substantially underrepresented, producing a worst-case class imbalance ratio of approximately 4.8 to 1 relative to the dominant classes. Reference keys, publication years, and license strings for all 16 source datasets are documented in the accompanying metadata file. Each source is listed individually in the Related Links section of this record and must be cited independently. To resolve the imbalance, synthetic images were generated for underrepresented classes using the DermGAN-DDPM hybrid generative pipeline, yielding a final balanced dataset with uniform class distribution across all five categories. The released files separate raw curated images from DermGAN-DDPM synthetic outputs, allowing researchers to use either collection depending on their experimental design. Users must consult the source licenses in the metadata before any commercial application, as one upstream source (Ali et al., 2024) carries a CC BY-NC 4.0 non-commercial restriction.