Evaluating the Effectiveness of Brain Tumor Image Generation using Generative Adversarial Network with Adam Optimizer

Abstract

Deep learning models known as Generative Adversarial Networks (GANs) have shown great potential in several applications, such as computer vision and image synthesis. They are now a viable tool in medical imaging, useful for tasks like improving diagnostic model performance, generating new images, and augmenting existing data. This paper aims to utilize the capabilities of GANs to produce synthetic MRI images, with the purpose of enhancing the training dataset for tumor classification. A new method is presented to classify tumors in MRI images by combining GANs and Convolutional Neural Networks (CNNs). This method employed the Adam optimizer and the Binary Cross Entropy (BCE) with Logits Loss as the criterion, where they contributed in optimizing the training process and stabilizing the GANs. The proposed method in this paper achieved an average accuracy of 95.1% and an average loss of 0.080 with large images. Furthermore, the proposed method is evaluated based on Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) and is compared to the existing models of GAN. These outcomes highlight the potential of the GAN-based approach in contributing to improved medical diagnostics and treatments.