# Classification of Brain Tumours in MR Images using Deep Spatiospatial Models

arXiv: https://arxiv.org/abs/2105.14071 · HTML: https://mlsanity.com/paper/2105.14071/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 38 references that are in the map.
- First posted on arXiv 28 May 2021.

## Builds on (most cited first, 9 of 9)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019)
- [Mixed Precision Training](https://mlsanity.com/paper/1710.03740/) (2017)
- [A Closer Look at Spatiotemporal Convolutions for Action Recognition](https://mlsanity.com/paper/1711.11248/) (2017)
- [Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge](https://mlsanity.com/paper/1811.02629/) (2018)
- [nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation](https://mlsanity.com/paper/1809.10486/) (2018)
- [TorchIO: A Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning](https://mlsanity.com/paper/2003.04696/) (2020)
- [Fine-tuning deep learning model parameters for improved super-resolution of dynamic MRI with prior-knowledge](https://mlsanity.com/paper/2102.02711/) (2021)

## Built on by (most cited first, 10 of 10)

- [Towards Generalist Foundation Model for Radiology by Leveraging Web-scale 2D&3D Medical Data](https://mlsanity.com/paper/2308.02463/) (2023)
- [UniBrain: Universal Brain MRI Diagnosis with Hierarchical Knowledge-enhanced Pre-training](https://mlsanity.com/paper/2309.06828/) (2023)
- [Towards More Efficient Data Valuation in Healthcare Federated Learning using Ensembling](https://mlsanity.com/paper/2209.05424/) (2022)
- [Weakly-supervised segmentation using inherently-explainable classification models and their application to brain tumour classification](https://mlsanity.com/paper/2206.05148/) (2022)
- [Liver Segmentation using Turbolift Learning for CT and Cone-beam C-arm Perfusion Imaging](https://mlsanity.com/paper/2207.10167/) (2022)
- [Optimizing Brain Tumor Classification: A Comprehensive Study on Transfer Learning and Imbalance Handling in Deep Learning Models](https://mlsanity.com/paper/2308.06821/) (2023)
- [Cross-modality Guidance-aided Multi-modal Learning with Dual Attention for MRI Brain Tumor Grading](https://mlsanity.com/paper/2401.09029/) (2024)
- [SeLIP: Similarity Enhanced Contrastive Language Image Pretraining for Multi-modal Head MRI](https://mlsanity.com/paper/2503.19801/) (2025)
- [Brain Tumor Detection using Swin Transformers](https://mlsanity.com/paper/2305.06025/) (2023)
- [VGG16-MCA UNet: Whole-Tumor Segmentation in 2D FLAIR MRI with Decoder-Side Channel Attention](https://mlsanity.com/paper/2609.23919/) (2026)

## Abstract (arXiv)

A brain tumour is a mass or cluster of abnormal cells in the brain, which has the possibility of becoming life-threatening because of its ability to invade neighbouring tissues and also form metastases. An accurate diagnosis is essential for successful treatment planning and magnetic resonance imaging is the principal imaging modality for diagnostic of brain tumours and their extent. Deep Learning methods in computer vision applications have shown significant improvement in recent years, most of which can be credited to the fact that a sizeable amount of data is available to train models on, and the improvements in the model architectures yielding better approximations in a supervised setting. Classifying tumours using such deep learning methods has made significant progress with the availability of open datasets with reliable annotations. Typically those methods are either 3D models, which use 3D volumetric MRIs or even 2D models considering each slice separately. However, by treating the slice spatial dimension separately, spatiotemporal models can be employed as spatiospatial models for this task. These models have the capabilities of learning specific spatial and temporal relationship, while reducing computational costs. This paper uses two spatiotemporal models, ResNet (2+1)D and ResNet Mixed Convolution, to classify different types of brain tumours. It was observed that both these models performed superior to the pure 3D convolutional model, ResNet18. Furthermore, it was also observed that pre-training the models on a different, even unrelated dataset before training them for the task of tumour classification improves the performance. Finally, Pre-trained ResNet Mixed Convolution was observed to be the best model in these experiments, achieving a macro F1-score of 0.93 and a test accuracy of 96.98%, while at the same time being the model with the least computational cost.