Brain Tumor Classification based on MR Images using GAN as a Pre-Trained Model

Yerukalareddy, Dinesh Reddy; Pavlovskiy, Evgeniy

doi:10.1109/csgb53040.2021.9496036

Cited by 12 publications

(2 citation statements)

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“…Researchers have applied the generative aspect of GAN to various tasks in medical image analysis, including classification [33], segmentation [34], de-noising [35], image reconstruction [36], and image synthesis [37]. The use of GAN as a data augmentation method has been shown to outperform various traditional augmentation methods.…”

Section: Related Workmentioning

confidence: 99%

The Use of Generative Adversarial Network and Graph Convolution Network for Neuroimaging-Based Diagnostic Classification

Huynh,

Yan,

et al. 2024

Brain Sciences

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Functional connectivity (FC) obtained from resting-state functional magnetic resonance imaging has been integrated with machine learning algorithms to deliver consistent and reliable brain disease classification outcomes. However, in classical learning procedures, custom-built specialized feature selection techniques are typically used to filter out uninformative features from FC patterns to generalize efficiently on the datasets. The ability of convolutional neural networks (CNN) and other deep learning models to extract informative features from data with grid structure (such as images) has led to the surge in popularity of these techniques. However, the designs of many existing CNN models still fail to exploit the relationships between entities of graph-structure data (such as networks). Therefore, graph convolution network (GCN) has been suggested as a means for uncovering the intricate structure of brain network data, which has the potential to substantially improve classification accuracy. Furthermore, overfitting in classifiers can be largely attributed to the limited number of available training samples. Recently, the generative adversarial network (GAN) has been widely used in the medical field for its generative aspect that can generate synthesis images to cope with the problems of data scarcity and patient privacy. In our previous work, GCN and GAN have been designed to investigate FC patterns to perform diagnosis tasks, and their effectiveness has been tested on the ABIDE-I dataset. In this paper, the models will be further applied to FC data derived from more public datasets (ADHD, ABIDE-II, and ADNI) and our in-house dataset (PTSD) to justify their generalization on all types of data. The results of a number of experiments show the powerful characteristic of GAN to mimic FC data to achieve high performance in disease prediction. When employing GAN for data augmentation, the diagnostic accuracy across ADHD-200, ABIDE-II, and ADNI datasets surpasses that of other machine learning models, including results achieved with BrainNetCNN. Specifically, in ADHD, the accuracy increased from 67.74% to 73.96% with GAN, in ABIDE-II from 70.36% to 77.40%, and in ADNI, reaching 52.84% and 88.56% for multiclass and binary classification, respectively. GCN also obtains decent results, with the best accuracy in ADHD datasets at 71.38% for multinomial and 75% for binary classification, respectively, and the second-best accuracy in the ABIDE-II dataset (72.28% and 75.16%, respectively). Both GAN and GCN achieved the highest accuracy for the PTSD dataset, reaching 97.76%. However, there are still some limitations that can be improved. Both methods have many opportunities for the prediction and diagnosis of diseases.

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Section: Related Workmentioning

confidence: 99%

The Use of Generative Adversarial Network and Graph Convolution Network for Neuroimaging-Based Diagnostic Classification

Huynh,

Yan,

et al. 2024

Brain Sciences

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“…A multi-class brain tumor classification issue uses synthesized images to augment the training set 72. Reddy et al proposed Multi-Scale Gradients GAN (MSG-GAN), which classifies tumors such as glioma, meningioma, pituitary, and no tumor to reduce misdiagnosis and effort spent annotating and segmenting images and the accuracy rate is 98.57% 73. Due to a scarcity of annotated medical images, Han et al proposed the Progressive Growth of Generative Adversarial Networks (PGGAN) to create realistic.…”

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confidence: 99%

Systematic survey on generative adversarial networks for brain tumor segmentation and classification

Kaur

Singh

Jindal

2023

Concurrency and Computation

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SummaryBrain cancer is one of the leading diseases of death in the world caused due to unwanted proliferation of cells. If left untreated, this growth can spread into other human body parts. So early detection helps to enhance long‐term survival and reduce the mortality rate. A lot of research has been done in the field of brain tumors. There have been numerous previous studies conducted using traditional approaches. So far, few review studies are available on brain tumor segmentation and classification and moreover, they were quite limited in explaining the connections of Generative Adversarial Networks (GAN) for brain tumor segmentation and classification. This review mainly focuses on the GAN architectures for brain tumor segmentation and classification. Therefore, first, an overview of brain tumors, GAN‐based publications, approaches to brain tumor segmentation and classification, data sets, software platforms, and performance parameters, are presented. Finally, GAN applications and current trends are discussed. This thorough survey will aid researchers and beginners in the future in creating a better decision support system.

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