# Computational Intelligence Approach to Improve the Classification Accuracy of Brain Neoplasm in MRI Data

arXiv: https://arxiv.org/abs/2101.09658 · HTML: https://mlsanity.com/paper/2101.09658/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 51 references that are in the map.
- First posted on arXiv 24 Jan 2021.

## Builds on (most cited first, 3 of 3)

- [Deep Bayesian Active Learning with Image Data](https://mlsanity.com/paper/1703.02910/) (2017)
- [Pyramid Stereo Matching Network](https://mlsanity.com/paper/1803.08669/) (2018)
- [MRI Tumor Segmentation with Densely Connected 3D CNN](https://mlsanity.com/paper/1802.02427/) (2018)

## Abstract (arXiv)

Automatic detection of brain neoplasm in Magnetic Resonance Imaging (MRI) is gaining importance in many medical diagnostic applications. This report presents two improvements for brain neoplasm detection in MRI data: an advanced preprocessing technique is proposed to improve the area of interest in MRI data and a hybrid technique using Convolutional Neural Network (CNN) for feature extraction followed by Support Vector Machine (SVM) for classification. The learning algorithm for SVM is modified with the addition of cost function to minimize false positive prediction addressing the errors in MRI data diagnosis. The proposed approach can effectively detect the presence of neoplasm and also predict whether it is cancerous (malignant) or non-cancerous (benign). To check the effectiveness of the proposed preprocessing technique, it is inspected visually and evaluated using training performance metrics. A comparison study between the proposed classification technique and the existing techniques was performed. The result showed that the proposed approach outperformed in terms of accuracy and can handle errors in classification better than the existing approaches.