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Data Overview: Comprehensive overview of the datasets examined within the DL literature review centered on brain tumor classification tasks and MRI data. Essential information regarding dimensionality, sample size, anatomical plane, MRI modalities, and pre-processing methods are summarized.

Journal: Cancers

Article Title: Advances in the Use of Deep Learning for the Analysis of Magnetic Resonance Image in Neuro-Oncology

doi: 10.3390/cancers16020300

Figure Lengend Snippet: Data Overview: Comprehensive overview of the datasets examined within the DL literature review centered on brain tumor classification tasks and MRI data. Essential information regarding dimensionality, sample size, anatomical plane, MRI modalities, and pre-processing methods are summarized.

Article Snippet: 137 , Muezzinoglu et al. [ ] (2023) , 2D , Kaggle-II , - , 3264 ( ) , - , - , Resizing, patch division , -.

Techniques: Transformation Assay, Sampling, Shear

Model Overview: Comprehensive summary of the DL architectures employed across the reviewed papers. The table outlines key information, including the brain tumor classification task, data partitioning, architecture, and the reported performance metrics.

Journal: Cancers

Article Title: Advances in the Use of Deep Learning for the Analysis of Magnetic Resonance Image in Neuro-Oncology

doi: 10.3390/cancers16020300

Figure Lengend Snippet: Model Overview: Comprehensive summary of the DL architectures employed across the reviewed papers. The table outlines key information, including the brain tumor classification task, data partitioning, architecture, and the reported performance metrics.

Article Snippet: 137 , Muezzinoglu et al. [ ] (2023) , 2D , Kaggle-II , - , 3264 ( ) , - , - , Resizing, patch division , -.

Techniques: Blocking Assay

An overview of publicly available MRI datasets for brain tumor classification benchmarking.

Journal: Cancers

Article Title: Advances in the Use of Deep Learning for the Analysis of Magnetic Resonance Image in Neuro-Oncology

doi: 10.3390/cancers16020300

Figure Lengend Snippet: An overview of publicly available MRI datasets for brain tumor classification benchmarking.

Article Snippet: 121 , AlTahhan et al. [ ] (2023) , 2D , Figshare Kaggle-II Kaggle-I , - - - , 2880 (H: 396, MN: 825, GL: 829, PT: 830) , - - - , T1c , - , -.

Techniques:

Data Overview: Comprehensive overview of the datasets examined within the DL literature review centered on brain tumor classification tasks and MRI data. Essential information regarding dimensionality, sample size, anatomical plane, MRI modalities, and pre-processing methods are summarized.

Journal: Cancers

Article Title: Advances in the Use of Deep Learning for the Analysis of Magnetic Resonance Image in Neuro-Oncology

doi: 10.3390/cancers16020300

Figure Lengend Snippet: Data Overview: Comprehensive overview of the datasets examined within the DL literature review centered on brain tumor classification tasks and MRI data. Essential information regarding dimensionality, sample size, anatomical plane, MRI modalities, and pre-processing methods are summarized.

Article Snippet: 121 , AlTahhan et al. [ ] (2023) , 2D , Figshare Kaggle-II Kaggle-I , - - - , 2880 (H: 396, MN: 825, GL: 829, PT: 830) , - - - , T1c , - , -.

Techniques: Transformation Assay, Sampling, Shear