Classification of Brain MRI Tumor Images Based on Deep Learning PGGAN Augmentation

Allah, Ahmed M. Gab; Sarhan, Amany M.; Elshennawy, Nada M.

doi:10.3390/diagnostics11122343

Cited by 39 publications

(25 citation statements)

References 43 publications

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“…While most data augmentation techniques aim to increase extraneous variance in the training set, deep learning can be used by itself, at least in theory, to increase meaningful variance. In a recent publication by Allah et al [ 44 ], a novel data augmentation method called a progressive growing generative adversarial network (PGGAN) was proposed and combined with rotation and flipping methods. The method involves an incremental increase of the size of the model during the training to produce MR images of brain tumors and to help overcome the shortage of images for deep learning training.…”

Section: Resultsmentioning

confidence: 99%

“…OLI-II vs. OLI-III DL-MajVot (AlexNet, VGG16, ResNet18, GoogleNet, ResNet50) 5-fold CV SEN = 100%, SPE = 100%, AUC = 1 100 4. LGG vs. HGG DL-MajVot (AlexNet, VGG16, ResNet18, GoogleNet, ResNet50) 5-fold CV SEN = 98.33%, SPE = 98.57%, AUC = 0.9845 98.43 Tandel et al [ 24 ] 2020 Normal vs. Tumorous Transfer learning with AlexNet Multiple CV (K2, K5, K10) RE = 100%, PRE = 100%, F1 score = 100% 100 Ayadi et al [ 98 ] 2021 Normal vs. Tumorous Custom CNN model 5-fold CV 100 Ye et al [ 126 ] 2022 Germinoma vs. Glioma Transfer learning with ResNet18 5-fold CV AUC = 0.88 81% 3 classes Allah et al [ 44 ] 2021 MEN vs. Glioma vs. PT PGGAN-augmentation VGG19 No info shared 98.54 Swati et al [ 50 ] 2019 …”

Section: Resultsmentioning

confidence: 99%

“…Overfitting may occur during the training process if the training dataset is not sufficiently large [ 39 ]. Therefore, many studies [ 40 , 41 , 42 , 43 , 44 ] use 2D brain image slices extracted from 3D brain MRI volumes to solve this problem, which increases the number of examples within the initial dataset and mitigates the class imbalance problem. In addition, it has the advantage of reducing the input data dimension and reducing the computational burden of training the network.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Data augmentation is another effective technique for increasing both the amount and the diversity of the training data by adding modified copies of existing data with commonly used morphological techniques, such as rotation, reflection (also referred to as flipping or mirroring), scaling, translation, and cropping [ 44 , 45 ]. Such strategies are based on the assumption that the size and orientation of image patches do not yield robust features for tumor classification.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Recently, well-established data augmentation techniques have begun to be supplemented by automatic methods that use deep learning approaches. For example, the authors in [ 44 ] proposed a progressively growing generative adversarial network (PGGAN) augmentation model to help overcome the shortage of images needed for CNN classification models. However, such methods are rare in the literature reviewed.…”

Section: Literature Reviewmentioning

confidence: 99%

See 4 more Smart Citations

Convolutional Neural Network Techniques for Brain Tumor Classification (from 2015 to 2022): Review, Challenges, and Future Perspectives

et al. 2022

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Convolutional neural networks (CNNs) constitute a widely used deep learning approach that has frequently been applied to the problem of brain tumor diagnosis. Such techniques still face some critical challenges in moving towards clinic application. The main objective of this work is to present a comprehensive review of studies using CNN architectures to classify brain tumors using MR images with the aim of identifying useful strategies for and possible impediments in the development of this technology. Relevant articles were identified using a predefined, systematic procedure. For each article, data were extracted regarding training data, target problems, the network architecture, validation methods, and the reported quantitative performance criteria. The clinical relevance of the studies was then evaluated to identify limitations by considering the merits of convolutional neural networks and the remaining challenges that need to be solved to promote the clinical application and development of CNN algorithms. Finally, possible directions for future research are discussed for researchers in the biomedical and machine learning communities. A total of 83 studies were identified and reviewed. They differed in terms of the precise classification problem targeted and the strategies used to construct and train the chosen CNN. Consequently, the reported performance varied widely, with accuracies of 91.63–100% in differentiating meningiomas, gliomas, and pituitary tumors (26 articles) and of 60.0–99.46% in distinguishing low-grade from high-grade gliomas (13 articles). The review provides a survey of the state of the art in CNN-based deep learning methods for brain tumor classification. Many networks demonstrated good performance, and it is not evident that any specific methodological choice greatly outperforms the alternatives, especially given the inconsistencies in the reporting of validation methods, performance metrics, and training data encountered. Few studies have focused on clinical usability.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

See 3 more Smart Citations

Convolutional Neural Network Techniques for Brain Tumor Classification (from 2015 to 2022): Review, Challenges, and Future Perspectives

et al. 2022

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A review of deep learning and Generative Adversarial Networks applications in medical image analysis

Sindhura,

Pai,

Bhat

et al. 2024

Multimedia Systems

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Nowadays, computer-aided decision support systems (CADs) for the analysis of images have been a perennial technique in the medical imaging field. In CADs, deep learning algorithms are widely used to perform tasks like classification, identification of patterns, detection, etc. Deep learning models learn feature representations from images rather than handcrafted features. Hence, deep learning models are quickly becoming the state-of-the-art method to achieve good performances in different computer-aided decision-support systems in medical applications. Similarly, deep learning-based generative models called Generative Adversarial Networks (GANs) have recently been developed as a novel method to produce realistic-looking synthetic data. GANs are used in different domains, including medical imaging generation. The common problems, like class imbalance and a small dataset, in healthcare are well addressed by GANs, and it is a leading area of research. Segmentation, reconstruction, detection, denoising, registration, etc. are the important applications of GANs. So in this work, the successes of deep learning methods in segmentation, classification, cell structure and fracture detection, computer-aided identification, and GANs in synthetic medical image generation, segmentation, reconstruction, detection, denoising, and registration in recent times are reviewed. Lately, the review article concludes by raising research directions for DL models and GANs in medical applications.

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Segmentation and identification of brain tumour in MRI images using PG-OneShot learning CNN model

Ali,

Wang,

Shi

2024

Multimed Tools Appl

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Classification of Brain MRI Tumor Images Based on Deep Learning PGGAN Augmentation

Cited by 39 publications

References 43 publications

Convolutional Neural Network Techniques for Brain Tumor Classification (from 2015 to 2022): Review, Challenges, and Future Perspectives

Convolutional Neural Network Techniques for Brain Tumor Classification (from 2015 to 2022): Review, Challenges, and Future Perspectives

A review of deep learning and Generative Adversarial Networks applications in medical image analysis

Segmentation and identification of brain tumour in MRI images using PG-OneShot learning CNN model

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