Generative AI Empowered Skin Cancer Diagnosis: Advancing Classification Through Deep Learning

Effective diagnostic techniques are still needed for the early diagnosis and treatment of skin cancer, which is still a serious health concern. This paper presents a Generative AI-powered approach for the diagnosis of skin cancer that uses deep learning techniques to enhance classification accuracy and reliability. Current diagnostic methods face challenges like class imbalance, limited dataset diversity, and dependency on manual labeling. Recent advancements in AI, including CNNs and GANs, offer promising solutions but are hindered by issues such as overfitting and lack of generalizability. By using a Convolutional Neural Network (CNN) for classification and training over 200 epochs, the model achieved an accuracy of 92%. Precision, recall, and F1-score are measures of evaluation metrics to ensure the performance of the model is coherent, for which data augmentation was used to handle class imbalance. A Generative Adversarial Network was used with this model to create synthetic images, thus augmenting 1000 samples over 600 epochs. This type of data augmentation made a difference in the generalization capability of the model and greatly contributed to more robust classification outcomes and advancements in the automated detection of skin cancer.