Exploring Radiologic Criteria for Glioma Grade Classification on the BraTS Dataset

Dequidt, Paul; Bourdon, Pascal; Tremblais, Benoît; Guillevin, Carole; Gianelli, Benoit; Billen, Claire; Cottier, Jean-Philippe; Vallée, Jean-Noël; Fernández-Maloigne, Christine; Guillevin, Rémy

doi:10.1016/j.irbm.2021.04.003

Cited by 24 publications

(10 citation statements)

References 21 publications

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“…Binary grading classifier is used for classification of HGG/LGG on BRATS-2020 dataset and provides an accuracy of 84.1% [ 64 ]. Features are extracted from the RNN model for the classification of tumor grades such as pituitary tumor [ 65 ] and meningioma and give an accuracy of 98.8% [ 66 ].…”

Section: Resultsmentioning

confidence: 99%

A New Model for Brain Tumor Detection Using Ensemble Transfer Learning and Quantum Variational Classifier

Amin

Anjum

Sharif

et al. 2022

Computational Intelligence and Neuroscience

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A brain tumor is an abnormal enlargement of cells if not properly diagnosed. Early detection of a brain tumor is critical for clinical practice and survival rates. Brain tumors arise in a variety of shapes, sizes, and features, with variable treatment options. Manual detection of tumors is difficult, time-consuming, and error-prone. Therefore, a significant requirement for computerized diagnostics systems for accurate brain tumor detection is present. In this research, deep features are extracted from the inceptionv3 model, in which score vector is acquired from softmax and supplied to the quantum variational classifier (QVR) for discrimination between glioma, meningioma, no tumor, and pituitary tumor. The classified tumor images have been passed to the proposed Seg-network where the actual infected region is segmented to analyze the tumor severity level. The outcomes of the reported research have been evaluated on three benchmark datasets such as Kaggle, 2020-BRATS, and local collected images. The model achieved greater than 90% detection scores to prove the proposed model's effectiveness.

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Section: Resultsmentioning

confidence: 99%

A New Model for Brain Tumor Detection Using Ensemble Transfer Learning and Quantum Variational Classifier

Amin

Anjum

Sharif

et al. 2022

Computational Intelligence and Neuroscience

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“…These datasets use advanced computer algorithms and machine learning techniques to automatically segment and classify different brain tissue types and abnormalities in MRI images. The existing methods have been evaluated on the available datasets like the Challenge series [1] , [2] , [3] . Different versions of available datasets provide pre-processed data with the subtraction of skull bones which makes it far different from the real-time acquired imaging data.…”

Section: Value Of the Datamentioning

confidence: 99%

Clinically acquired new challenging dataset for brain SOL segmentation: AJBDS-2023

Amin,

Anjum,

Gul

et al. 2024

Data in Brief

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“…BraTS-21 EfficientNetB0 [38] 55.90% YOLOv5 [43] 88.00% VGG19 [44] 90.03% SVM [45] 84.10% Pretrained CNN [46] 92.67% LCDEiT (Proposed) 93.69% This reflects the strong applicability of image transformers with a robust learner in the medical imaging field where faster computation is a crucial criterion to initiate treatment of the critical patient. In the future, the imbalance dataset handling approach such as class-wise augmentation could be implemented to overcome the issues related to a greater misclassification rate for lower sample classes.…”

Section: Model Test Accuracymentioning

confidence: 99%

LCDEiT: A Linear Complexity Data-Efficient Image Transformer for MRI Brain Tumor Classification

et al. 2023

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Current deep learning-assisted brain tumor classification models sustain inductive bias and parameter dependency problems for extracting texture-based image information. Thereby concerning these problems, the recent development of the vision transformer model has substituted the DL model for classification tasks. However, the high performance of the vision transformer model depends on a large-scale dataset as well as self-attention calculations between the number of image patches which result in a quadratic computational complexity. To address these problems, the vision transformer must be data-efficient to be well-trained with a limited amount of data, and the computational complexity must be linear with the number of image patches. Consequently, this paper presents a novel linear-complexity data-efficient image transformer called LCDEiT for training with small-size datasets by using a teacher-student strategy and linear computational complexity concerning the number of patches using an external attention mechanism. The teacher model comprised a custom gated-pooled convolutional neural network to provide knowledge to the transformer-based student model for the classification of MRI brain tumors. The average classification accuracy and F1-score for two benchmark datasets including Figshare and BraTS-21 are found 98.11% and 97.86% and 93.69% and 93.68% respectively. The results indicate that the proposed model could have a great impact on medical imaging-based diagnosis where data availability and faster computations are the main concern.

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Exploring Radiologic Criteria for Glioma Grade Classification on the BraTS Dataset

Cited by 24 publications

References 21 publications

A New Model for Brain Tumor Detection Using Ensemble Transfer Learning and Quantum Variational Classifier

A New Model for Brain Tumor Detection Using Ensemble Transfer Learning and Quantum Variational Classifier

Clinically acquired new challenging dataset for brain SOL segmentation: AJBDS-2023

LCDEiT: A Linear Complexity Data-Efficient Image Transformer for MRI Brain Tumor Classification

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