Abstract

Alzheimer’s disease (AD) early diagnosis plays a pivotal role in effective intervention and patient care. With limited and noisy medical imaging datasets, GANs are utilized to generate synthetic brain images, aiding in the augmentation of existing data. The selected classification algorithms are well-established in computer vision and have demonstrated efficacy in image classification tasks. The GAN is employed for data augmentation, creating synthetic images representative of AD-associated features. Subsequently, the augmented dataset is utilized to train and evaluate the performance of multiple classification algorithms, providing a comprehensive analysis of their effectiveness in AD detection. This research contributes to the field of Alzheimer’s disease diagnosis by integrating GANs for data augmentation and evaluating the performance of ten diverse classification algorithms, offering insights into their suitability for early detection. In this study, we leverage Generative Adversarial Networks (GANs) for data augmentation in medical imaging, enhancing the quality and diversity of brain images associated with AD. Various classification algorithms, including AlexNet, GoogleNet, VGG 16, VGG 19, ResNet 18, ResNet 50, ResNet 101, ShuffleNet, MobileNet, and DenseNet 201, are employed for robust AD detection. Our experiments demonstrate improved classification accuracy and robustness due to GAN-based data augmentation. Among the classification algorithms, ResNet 50 and DenseNet 201 exhibit superior performance, showcasing their potential in accurate and reliable early AD diagnosis.