# Robust Brain MRI Image Classification with SIBOW-SVM

arXiv: https://arxiv.org/abs/2311.08908 · HTML: https://mlsanity.com/paper/2311.08908/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 111 references that are in the map.
- First posted on arXiv 15 Nov 2023.

## Builds on (most cited first, 16 of 16)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [Revisiting the Calibration of Modern Neural Networks](https://mlsanity.com/paper/2106.07998/) (2021)
- [Soft-to-Hard Vector Quantization for End-to-End Learning Compressible Representations](https://mlsanity.com/paper/1704.00648/) (2017)
- [Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning](https://mlsanity.com/paper/1710.04043/) (2017)
- [Survey of Dropout Methods for Deep Neural Networks](https://mlsanity.com/paper/1904.13310/) (2019)
- [Smoothing parameter and model selection for general smooth models](https://mlsanity.com/paper/1511.03864/) (2015)
- [Advancements in Image Classification using Convolutional Neural Network](https://mlsanity.com/paper/1905.03288/) (2019)
- [Deep convolutional neural networks for brain image analysis on magnetic resonance imaging: a review](https://mlsanity.com/paper/1712.03747/) (2017)
- [Restructuring Batch Normalization to Accelerate CNN Training](https://mlsanity.com/paper/1807.01702/) (2018)
- [Capsule Networks for Brain Tumor Classification based on MRI Images and Course Tumor Boundaries](https://mlsanity.com/paper/1811.00597/) (2018)
- [Linear Algorithms for Robust and Scalable Nonparametric Multiclass Probability Estimation](https://mlsanity.com/paper/2205.12460/) (2022)
- [Keep It Simple: CNN Model Complexity Studies for Interference Classification Tasks](https://mlsanity.com/paper/2303.03326/) (2023)

## 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)
- [Sparse Learning and Class Probability Estimation with Weighted Support Vector Machines](https://mlsanity.com/paper/2312.10618/) (2023)

## Abstract (arXiv)

The majority of primary Central Nervous System (CNS) tumors in the brain are among the most aggressive diseases affecting humans. Early detection of brain tumor types, whether benign or malignant, glial or non-glial, is critical for cancer prevention and treatment, ultimately improving human life expectancy. Magnetic Resonance Imaging (MRI) stands as the most effective technique to detect brain tumors by generating comprehensive brain images through scans. However, human examination can be error-prone and inefficient due to the complexity, size, and location variability of brain tumors. Recently, automated classification techniques using machine learning (ML) methods, such as Convolutional Neural Network (CNN), have demonstrated significantly higher accuracy than manual screening, while maintaining low computational costs. Nonetheless, deep learning-based image classification methods, including CNN, face challenges in estimating class probabilities without proper model calibration. In this paper, we propose a novel brain tumor image classification method, called SIBOW-SVM, which integrates the Bag-of-Features (BoF) model with SIFT feature extraction and weighted Support Vector Machines (wSVMs). This new approach effectively captures hidden image features, enabling the differentiation of various tumor types and accurate label predictions. Additionally, the SIBOW-SVM is able to estimate the probabilities of images belonging to each class, thereby providing high-confidence classification decisions. We have also developed scalable and parallelable algorithms to facilitate the practical implementation of SIBOW-SVM for massive images. As a benchmark, we apply the SIBOW-SVM to a public data set of brain tumor MRI images containing four classes: glioma, meningioma, pituitary, and normal. Our results show that the new method outperforms state-of-the-art methods, including CNN.