# Brain Tumor Type Classification via Capsule Networks

arXiv: https://arxiv.org/abs/1802.10200 · HTML: https://mlsanity.com/paper/1802.10200/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Feb 2018.

## Built on by (most cited first, 22 of 22)

- [Interpretable Medical Image Classification using Prototype Learning and Privileged Information](https://mlsanity.com/paper/2310.15741/) (2023)
- [MAG-Net: Multi-task attention guided network for brain tumor segmentation and classification](https://mlsanity.com/paper/2107.12321/) (2021)
- [Towards Efficient Capsule Networks](https://mlsanity.com/paper/2208.09203/) (2022)
- [Applications of interpretable deep learning in neuroimaging: a comprehensive review](https://mlsanity.com/paper/2406.17792/) (2024)
- [Hybridization of Capsule and LSTM Networks for unsupervised anomaly detection on multivariate data](https://mlsanity.com/paper/2202.05538/) (2022)
- [Brain Tumor Segmentation from MRI Images using Deep Learning Techniques](https://mlsanity.com/paper/2305.00257/) (2023)
- [Triplet Contrastive Learning for Brain Tumor Classification](https://mlsanity.com/paper/2108.03611/) (2021)
- [Data Augmentation using Feature Generation for Volumetric Medical Images](https://mlsanity.com/paper/2209.14097/) (2022)
- [ParseCaps: An Interpretable Parsing Capsule Network for Medical Image Diagnosis](https://mlsanity.com/paper/2411.01564/) (2024)
- [Evaluation of Vision Transformers for Multimodal Image Classification: A Case Study on Brain, Lung, and Kidney Tumors](https://mlsanity.com/paper/2502.05517/) (2025)
- [Parallel Capsule Networks for Classification of White Blood Cells](https://mlsanity.com/paper/2108.02644/) (2021)
- [A Novel Capsule Neural Network Based Model for Drowsiness Detection Using Electroencephalography Signals](https://mlsanity.com/paper/2204.01666/) (2022)
- [Supervised Dimensionality Reduction and Image Classification Utilizing Convolutional Autoencoders](https://mlsanity.com/paper/2208.12152/) (2022)
- [Capsule network with shortcut routing](https://mlsanity.com/paper/2307.10212/) (2023)
- [1D-CapsNet-LSTM: A Deep Learning-Based Model for Multi-Step Stock Index Forecasting](https://mlsanity.com/paper/2310.02090/) (2023)
- [InceptionCaps: A Performant Glaucoma Classification Model for Data-scarce Environment](https://mlsanity.com/paper/2312.00803/) (2023)
- [HistoSegCap: Capsules for Weakly-Supervised Semantic Segmentation of Histological Tissue Type in Whole Slide Images](https://mlsanity.com/paper/2402.10851/) (2024)
- [Robust Influence-based Training Methods for Noisy Brain MRI](https://mlsanity.com/paper/2403.10698/) (2024)
- [One-Shot Identification with Different Neural Network Approaches](https://mlsanity.com/paper/2601.08278/) (2026)
- [PhyDCM: A Reproducible Open-Source Framework for AI-Assisted Brain Tumor Classification from Multi-Sequence MRI](https://mlsanity.com/paper/2603.26794/) (2026)
- [Multiple Additive Neural Networks for Structured and Unstructured Data](https://mlsanity.com/paper/2604.26888/) (2026)
- [ORB-SVM : An Innovative Hybrid Framework for Efficient Brain Tumor Detection from MRI Scans](https://mlsanity.com/paper/2609.02333/) (2026)

## Abstract (arXiv)

Brain tumor is considered as one of the deadliest and most common form of cancer both in children and in adults. Consequently, determining the correct type of brain tumor in early stages is of significant importance to devise a precise treatment plan and predict patient's response to the adopted treatment. In this regard, there has been a recent surge of interest in designing Convolutional Neural Networks (CNNs) for the problem of brain tumor type classification. However, CNNs typically require large amount of training data and can not properly handle input transformations. Capsule networks (referred to as CapsNets) are brand new machine learning architectures proposed very recently to overcome these shortcomings of CNNs, and posed to revolutionize deep learning solutions. Of particular interest to this work is that Capsule networks are robust to rotation and affine transformation, and require far less training data, which is the case for processing medical image datasets including brain Magnetic Resonance Imaging (MRI) images. In this paper, we focus to achieve the following four objectives: (i) Adopt and incorporate CapsNets for the problem of brain tumor classification to design an improved architecture which maximizes the accuracy of the classification problem at hand; (ii) Investigate the over-fitting problem of CapsNets based on a real set of MRI images; (iii) Explore whether or not CapsNets are capable of providing better fit for the whole brain images or just the segmented tumor, and; (iv) Develop a visualization paradigm for the output of the CapsNet to better explain the learned features. Our results show that the proposed approach can successfully overcome CNNs for the brain tumor classification problem.