An MRI-based deep learning approach for efficient classification of brain tumors

“…(2021) 19 Brain tumor Histopathology 5-fold cross-validation Yes No Haq et al. (2021) 20 Brain tumor Histopathology Random split-sample validation No No Raghavendra et al. (2021) 21 Brain tumor Histopathology 10-fold cross-validation No No Chakrabarty et al.…”

Section: Resultsmentioning

confidence: 99%

Performance of deep learning algorithms to distinguish high-grade glioma from low-grade glioma: A systematic review and meta-analysis

et al. 2023

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“…Another study focused on the automated identification and classification of brain tumor MRI data classified into glioma, meningioma, and pituitary tumors, with an accuracy of 93.7% and a DICE similarity coefficient (DSC) of 95.8%. 23 A subsequent task of classifying gliomas into high or low grade had an accuracy of 96.5% and a DSC of 94.3%. These models were based on the GoogleNet variant architectures to efficiently combine local features.…”

Section: Brain Tumorsmentioning

confidence: 99%

“…These results show good to excellent agreement between the gold standard BRATS dataset and Sens.AI DCE, with a total execution time of approximately 3 min. Another study focused on the automated identification and classification of brain tumor MRI data classified into glioma, meningioma, and pituitary tumors, with an accuracy of 93.7% and a DICE similarity coefficient (DSC) of 95.8% 23 . A subsequent task of classifying gliomas into high or low grade had an accuracy of 96.5% and a DSC of 94.3%.…”

Section: Mri DL Studies Of the Brainmentioning

confidence: 99%

Developing and deploying deep learning models in brain magnetic resonance imaging: A review

et al. 2023

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Magnetic resonance imaging (MRI) of the brain has benefited from deep learning (DL) to alleviate the burden on radiologists and MR technologists, and improve throughput. The easy accessibility of DL tools has resulted in a rapid increase of DL models and subsequent peer‐reviewed publications. However, the rate of deployment in clinical settings is low. Therefore, this review attempts to bring together the ideas from data collection to deployment in the clinic, building on the guidelines and principles that accreditation agencies have espoused. We introduce the need for and the role of DL to deliver accessible MRI. This is followed by a brief review of DL examples in the context of neuropathologies. Based on these studies and others, we collate the prerequisites to develop and deploy DL models for brain MRI. We then delve into the guiding principles to develop good machine learning practices in the context of neuroimaging, with a focus on explainability. A checklist based on the United States Food and Drug Administration's good machine learning practices is provided as a summary of these guidelines. Finally, we review the current challenges and future opportunities in DL for brain MRI.

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“…With the rise of convolutional neural network (CNN) deep learning model architectures since 2012, in addition to emerging advanced computational resources, such as GPUs and TPUs, during the past decade, several methods have been proposed for the classification of brain tumors based on the finetuning of the existing state-of-the-art CNN models, such as AlexNet, VGG16, ResNets, Inception, DenseNets, and Xception, which had already been found to be successful for various computer vision tasks [ 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 ]. These aforementioned pretrained CNN models based on localized convolutions demonstrated excellent performance in brain tumor classification across different datasets [ 23 , 24 , 25 , 26 ]. In a recent study, variational autoencoders, along with generative adversarial networks, were used for synthetic data generation, and ResNet50 was used for tumor classification [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

Classification of Brain Tumor from Magnetic Resonance Imaging Using Vision Transformers Ensembling

et al. 2022

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The automated classification of brain tumors plays an important role in supporting radiologists in decision making. Recently, vision transformer (ViT)-based deep neural network architectures have gained attention in the computer vision research domain owing to the tremendous success of transformer models in natural language processing. Hence, in this study, the ability of an ensemble of standard ViT models for the diagnosis of brain tumors from T1-weighted (T1w) magnetic resonance imaging (MRI) is investigated. Pretrained and finetuned ViT models (B/16, B/32, L/16, and L/32) on ImageNet were adopted for the classification task. A brain tumor dataset from figshare, consisting of 3064 T1w contrast-enhanced (CE) MRI slices with meningiomas, gliomas, and pituitary tumors, was used for the cross-validation and testing of the ensemble ViT model’s ability to perform a three-class classification task. The best individual model was L/32, with an overall test accuracy of 98.2% at 384 × 384 resolution. The ensemble of all four ViT models demonstrated an overall testing accuracy of 98.7% at the same resolution, outperforming individual model’s ability at both resolutions and their ensembling at 224 × 224 resolution. In conclusion, an ensemble of ViT models could be deployed for the computer-aided diagnosis of brain tumors based on T1w CE MRI, leading to radiologist relief.

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An MRI-based deep learning approach for efficient classification of brain tumors

Cited by 33 publications

References 68 publications

Performance of deep learning algorithms to distinguish high-grade glioma from low-grade glioma: A systematic review and meta-analysis

Performance of deep learning algorithms to distinguish high-grade glioma from low-grade glioma: A systematic review and meta-analysis

Developing and deploying deep learning models in brain magnetic resonance imaging: A review

Classification of Brain Tumor from Magnetic Resonance Imaging Using Vision Transformers Ensembling

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