DC-AL GAN: Pseudoprogression and True Tumor Progression of Glioblastoma Multiform Image Classification Based on DCGAN and AlexNet

TL;DR

This paper introduces DC-AL GAN, a novel deep learning approach combining DCGAN and AlexNet to accurately differentiate between pseudoprogression and true tumor progression in glioblastoma MRI images, aiding clinical decision-making.

Contribution

The paper presents a new feature learning method that fuses high-level and low-level features from DCGAN and AlexNet for improved classification of PsP and TTP.

Findings

DC-AL GAN outperforms existing methods in classification accuracy.

High-level feature fusion enhances discriminative power.

The approach demonstrates robustness in differentiating PsP from TTP.

Abstract

Pseudoprogression (PsP) occurs in 20-30% of patients with glioblastoma multiforme (GBM) after receiving the standard treatment. In the course of post-treatment magnetic resonance imaging (MRI), PsP exhibits similarities in shape and intensity to the true tumor progression (TTP) of GBM. So, these similarities pose challenges on the differentiation of these types of progression and hence the selection of the appropriate clinical treatment strategy. In this paper, we introduce DC-AL GAN, a novel feature learning method based on deep convolutional generative adversarial network (DCGAN) and AlexNet, to discriminate between PsP and TTP in MRI images. Due to the adversarial relationship between the generator and the discriminator of DCGAN, high-level discriminative features of PsP and TTP can be derived for the discriminator with AlexNet. Also, a feature fusion scheme is used to combine higher-layer features with lower-layer information, leading to more powerful features that are used for effectively discriminating between PsP and TTP. The experimental results show that DC-AL GAN achieves desirable PsP and TTP classification performance that is superior to other state-of-the-art methods.