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Brain tumor multi classification and segmentation in MRI images using deep learning

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arxiv 2304.10039 v2 pith:OWQHFWHA submitted 2023-04-20 eess.IV cs.CVcs.LG

Belal Amin , Romario Sameh Samir , Youssef Tarek , Mohammed Ahmed , Rana Ibrahim , Manar Ahmed , Mohamed Hassan This is my paper
classification eess.IVcs.CVcs.LG
keywords segmentationbrainclassificationimagesmodeltumorarchitecturedeep
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This study proposes a deep learning model for the classification and segmentation of brain tumors from magnetic resonance imaging (MRI) scans. The classification model is based on the EfficientNetB1 architecture and is trained to classify images into four classes: meningioma, glioma, pituitary adenoma, and no tumor. The segmentation model is based on the U-Net architecture and is trained to accurately segment the tumor from the MRI images. The models are evaluated on a publicly available dataset and achieve high accuracy and segmentation metrics, indicating their potential for clinical use in the diagnosis and treatment of brain tumors.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Hierarchical Deep Feature Fusion and Ensemble Learning for Enhanced Brain Tumor MRI Classification

    cs.CV 2025-06 reject novelty 4.0 of 10

    A ViT feature ensemble plus ML classifier voting pipeline is evaluated on two binary brain MRI datasets, reporting up to 99.8% accuracy without a same-dataset comparison against prior methods.

  2. Deep Brain Net: An Optimized Deep Learning Model for Brain tumor Detection in MRI Images Using EfficientNetB0 and ResNet50 with Transfer Learning

    eess.IV 2025-07 reject novelty 2.0 of 10

    Combining EfficientNetB0 and ResNet50 with transfer learning yields an 88 percent accuracy claim for four-class brain tumor MRI classification, but the reported metrics are internally inconsistent and the model is not...

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