Generative Adversarial Synthesis and Deep Feature Discrimination of Brain Tumor MRI Images

TL;DR

This paper presents a deep learning approach using DC-GANs to generate synthetic brain MRI images, and employs CNNs to classify tumors, demonstrating that synthetic data can effectively augment limited real datasets.

Contribution

The study introduces a novel combination of DC-GANs and CNNs for synthetic MRI data generation and tumor classification, validating the utility of synthetic images in medical imaging tasks.

Findings

Synthetic MRI images are realistic and useful for training.

CNN achieves comparable accuracy on synthetic and real data.

Synthetic data can mitigate limited dataset issues in medical imaging.

Abstract

Compared to traditional methods, Deep Learning (DL) becomes a key technology for computer vision tasks. Synthetic data generation is an interesting use case for DL, especially in the field of medical imaging such as Magnetic Resonance Imaging (MRI). The need for this task since the original MRI data is limited. The generation of realistic medical images is completely difficult and challenging. Generative Adversarial Networks (GANs) are useful for creating synthetic medical images. In this paper, we propose a DL based methodology for creating synthetic MRI data using the Deep Convolutional Generative Adversarial Network (DC-GAN) to address the problem of limited data. We also employ a Convolutional Neural Network (CNN) classifier to classify the brain tumor using synthetic data and real MRI data. CNN is used to evaluate the quality and utility of the synthetic images. The classification result demonstrates comparable performance on real and synthetic images, which validates the effectiveness of GAN-generated images for downstream tasks.