Generating synthetic data in biomedical imaging by designing GANs

Recent advances in deep learning techniques have made medical analysis available with enhanced accuracy and efficiency, where brain tumor classification is automatically identified in an influential role. Hence, one of the synthesized approaches of an innovative idea to use GANs in this paper development is the synthesis of T1-weighted and post-contrast ischemic stroke brain MRIs to increase performance in the classification of the mentioned diseases according to deep learning. This paper, therefore, has the following objective: to evaluate the efficiency of GAN-generated images in learning deep in the transfer learning models and the performance in both tumor and non-tumor brain images. We use the two main architectures of GAN in our process: Vanilla and Deep Convolutional GAN (DCGAN). Details of the three major deep transfer learning models below portray the Convolutional Neural Network (CNN), MobileNetV2, and ResNet152v2. This learned weight would become a pre-trained representation of the models combined with the augmented dataset for feature extraction and classification purposes. I.e., where transfer learning is applied in the models, it is way more accessible for those architectures of the neural network to tap into the knowledge learned by the former from large-scale datasets and adapt it for tasks at hand on classifying brain tumors. Concerning training and validation, Python programming language integrated with the Keras deep learning framework was employed to implement the indicated operations. In terms of training, GPU processing power was available to allow the model to learn faster. In this regard, this was incorporated with the GPU processing by using the NVIDIA GeForce RTX 2060 GPU. Both Vanilla GAN and DCGAN have counterparts when generating images.