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

arXiv: https://arxiv.org/abs/2304.10039 · HTML: https://mlsanity.com/paper/2304.10039/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 13 references that are in the map.
- First posted on arXiv 20 Apr 2023.

## Builds on (most cited first, 2 of 2)

- [Brain Tumor Segmentation with Deep Neural Networks](https://mlsanity.com/paper/1505.03540/) (2015)
- [Transfer Learning for Domain Adaptation in MRI: Application in Brain Lesion Segmentation](https://mlsanity.com/paper/1702.07841/) (2017)

## Built on by (most cited first, 2 of 2)

- [Hierarchical Deep Feature Fusion and Ensemble Learning for Enhanced Brain Tumor MRI Classification](https://mlsanity.com/paper/2506.12363/) (2025)
- [Deep Brain Net: An Optimized Deep Learning Model for Brain tumor Detection in MRI Images Using EfficientNetB0 and ResNet50 with Transfer Learning](https://mlsanity.com/paper/2507.07011/) (2025)

## Abstract (arXiv)

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.