# A Deep Learning Approach for Brain Tumor Classification and Segmentation Using a Multiscale Convolutional Neural Network

arXiv: https://arxiv.org/abs/2402.05975 · HTML: https://mlsanity.com/paper/2402.05975/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 31 references that are in the map.
- First posted on arXiv 4 Feb 2024.

## Builds on (most cited first, 6 of 6)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation](https://mlsanity.com/paper/1511.00561/) (2015)
- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [Brain Tumor Segmentation with Deep Neural Networks](https://mlsanity.com/paper/1505.03540/) (2015)
- [Deep learning in radiology: an overview of the concepts and a survey of the state of the art](https://mlsanity.com/paper/1802.08717/) (2018)

## Built on by (most cited first, 7 of 7)

- [Hierarchical Deep Feature Fusion and Ensemble Learning for Enhanced Brain Tumor MRI Classification](https://mlsanity.com/paper/2506.12363/) (2025)
- [Hybrid Ensemble Approaches: Optimal Deep Feature Fusion and Hyperparameter-Tuned Classifier Ensembling for Enhanced Brain Tumor Classification](https://mlsanity.com/paper/2507.12177/) (2025)
- [Deep Fusion Model for Brain Tumor Classification Using Fine-Grained Gradient Preservation](https://mlsanity.com/paper/2406.19690/) (2024)
- [Systematic Review of Pituitary Gland and Pituitary Adenoma Automatic Segmentation Techniques in Magnetic Resonance Imaging](https://mlsanity.com/paper/2506.19797/) (2025)
- [Uncovering Neuroimaging Biomarkers of Brain Tumor Surgery with AI-Driven Methods](https://mlsanity.com/paper/2507.04881/) (2025)
- [ResLink: A Novel Deep Learning Architecture for Brain Tumor Classification with Area Attention and Residual Connections](https://mlsanity.com/paper/2508.17259/) (2025)
- [Research on Brain Tumor Classification Method Based on Improved ResNet34 Network](https://mlsanity.com/paper/2512.03751/) (2025)

## Abstract (arXiv)

In this paper, we present a fully automatic brain tumor segmentation and classification model using a Deep Convolutional Neural Network that includes a multiscale approach. One of the differences of our proposal with respect to previous works is that input images are processed in three spatial scales along different processing pathways. This mechanism is inspired in the inherent operation of the Human Visual System. The proposed neural model can analyze MRI images containing three types of tumors: meningioma, glioma, and pituitary tumor, over sagittal, coronal, and axial views and does not need preprocessing of input images to remove skull or vertebral column parts in advance. The performance of our method on a publicly available MRI image dataset of 3064 slices from 233 patients is compared with previously classical machine learning and deep learning published methods. In the comparison, our method remarkably obtained a tumor classification accuracy of 0.973, higher than the other approaches using the same database.