GAN-Alz: Synthetic Data Generation for Multiclass Alzheimer’s Classification

Accurate detection of skin cancer, particularly melanoma, is crucial for effective treatment and patient survival. This study explores the use of Convolutional Neural Networks (CNNs) enhanced by Generative Adversarial Networks (GANs) for skin cancer detection. CNNs have shown remarkable performance in image classification tasks but often require extensive labeled datasets, which are scarce in medical domains. To address this, GANs are employed to generate synthetic skin lesion images, augmenting the dataset and improving the model’s generalization capability. The proposed approach integrates a pre-trained CNN architecture fine-tuned with a combination of real and GANgenerated images, enhancing the network’s ability to distinguish between benign and malignant lesions. Experimental results demonstrate that the inclusion of GAN-augmented data improves the model’s accuracy by $7 \%$, achieving an overall accuracy of $94 \%$, with precision and recall rates of $92 \%$ and $93 \%$, respectively. These findings suggest that the GAN-augmented CNN model offers a promising solution for automated skin cancer screening