# Enhancing meningioma tumor classification accuracy through multi-task learning approach and image analysis of MRI images

## TL;DR

This study improves meningioma tumor classification accuracy using a multi-task learning approach with MRI images and patient data.

## Contribution

A novel multi-task learning framework that combines MRI data with demographic information to enhance meningioma classification.

## Key findings

- The MTL model achieved 99.6% ± 0.2 accuracy on test data.
- The approach outperformed single-task learning baselines significantly.
- The method shows potential for clinical decision-making and personalized treatment.

## Abstract

Accurate classification of meningioma brain tumors is crucial for determining the appropriate treatment plan and improving patient outcomes. However, this task is challenging due to the slow-growing nature of these tumors and the potential for misdiagnosis. Additionally, deep learning models for tumor classification often require large amounts of labeled data, which can be costly and time-consuming to obtain, especially in the medical domain.

Our main aim is to enhance Meningioma Tumor Classification Accuracy.

This study proposes a multi-task learning (MTL) approach to enhance the accuracy of meningioma tumor classification while mitigating the need for excessive labeled data. The primary task involves classifying meningioma tumors based on MRI imaging data, while auxiliary tasks leverage patient demographic information, such as age and gender. By incorporating these additional data sources into the learning process, the proposed MTL framework leverages the interdependencies among multiple tasks to improve overall prediction accuracy. The study evaluates the performance of the MTL approach using a dataset of 2218 brain MRI images from 34 patients diagnosed with meningioma, obtained from the Mahdia Imaging Center in Hamadan, Iran.

Results demonstrate that the MTL model significantly outperforms single-task learning baselines, achieving 99.6% ± 0.2 accuracy on the test data in 95% confidence interval.

This highlights the efficacy of the proposed approach in enhancing meningioma tumor classification and its potential for aiding clinical decision-making and personalized treatment planning.

Our proposed method can be used in computer-aided diagnosis systems.