International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Volume 7 Issue 3
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Enhancing Skin Disease Classification Through GAN-Generated Synthetic Images for Improved CNN Training and Generalization

Author(s) Ms. Senjuti Ghosal, Dr.Senthil MuruganV, Neha Bharadwaj, Relli Naga Sai
Country India
Abstract The research introduces GANs to CNNs for improving skin disease classification tasks. GANs produce synthetic realistic images of scarce skin lesions to tackle both data insufficiency and class imbalance problems. When using real and synthetic data for training the CNN model it generates better accuracy along with improved generalization mainly toward the identification of rare conditions including actinic keratosis and vascular lesions. The model performance receives enhancement from WeightedRandomSampler in addition to mixed precision training which achieves both class balancing and training acceleration. Advanced techniques and methods help the system produce accurate diagnoses and generalize to diagnose rare skin diseases more effectively. Data synthesis proves its worth as an addition to medical image classification operations by enabling fewer annotated medical image datasets. Flask and Streamlit enabled real-time deployment which creates an easy-to-use platform for healthcare staff allowing the solution to scale for professional use. The medical field can experience a breakthrough through this method which allows immediate and trustworthy identification of skin diseases. The solution provides meaningful benefits to populations that lack enough skilled dermatologists which results in enhanced healthcare results for patients. This project delivers an effective model which enables early accurate skin disease diagnosis that supports better patient care in dermatology fields
Keywords Skin Disease Classification, Generative Adversarial Networks (GANs), Convolutional Neural Networks (CNNs), Data Augmentation, Class Imbalance, Real-Time Inference, Mixed-Precision Training, WeightedRandomSampler, Synthetic Data, Early Diagnosis.
Field Engineering
Published In Volume 7, Issue 3, May-June 2025
Published On 2025-05-11
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E-ISSN 2582-2160
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