Brain tumour image generation based on the deep convolutional generative adversarial network.

The generation for brain tumour is a challenging task, which attracts much attention in recent years. This study builds a Deep Convolutional Generative Adversarial Network (DCGAN) using TensorFlow to generate new images. The code imports TensorFlow, layers, loss functions, and libraries such as matplotlib, numpy, PIL (Python Imaging Library), and time. The dataset contains images of 28×28 pixels, which are resized and normalized to be between 0 and 1. The Generative Adversarial Network (GAN) model consists of a generator network and a discriminator network, where the generator produces new images, and the discriminator distinguishes between the generated and real images. The generator network is made up of sequential layers, including dense, batch normalization, leaky ReLU, and convolutional transpose layers. The discriminator network has convolutional and dense layers. The study creates a random normal distribution and generates new images from it. The training loop includes discriminator and generator loss functions, where the discriminator loss function is the binary cross-entropy between the true and fake outputs, and the generator loss function is binary cross-entropy between the generated and true outputs. Finally, the study trains the model for a set number of epochs to generate new images. The results demonstrated the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]