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Deep Learning for Brain Tumor Classification.

Justin S Paul¹, Andrew J Plassard¹, Bennett A Landman^1,2

¹Computer Science, Vanderbilt University, Nashville, TN, USA 37235.

Proceedings of Spie--The International Society for Optical Engineering

|March 9, 2026

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Deep learning accurately classifies brain tumors using MRI images. This approach achieved over 91% accuracy, outperforming specialized methods for meningioma, glioma, and pituitary tumor detection.

Area of Science:

Medical Imaging
Artificial Intelligence
Neuroscience

Background:

Deep learning (DL) shows promise in supervised machine learning and image classification.
Accurate brain tumor classification is crucial for effective treatment planning.

Purpose of the Study:

To apply deep learning methods for classifying brain images of meningioma, glioma, and pituitary tumors.
To evaluate the performance of fully connected and convolutional neural networks on this task.

Main Methods:

Utilized a dataset of 989 axial T1-weighted contrast-enhanced MRI (CE-MRI) images from 191 patients.
Trained and tested fully connected and convolutional neural networks, including data augmentation.
Employed a five-fold cross-validation strategy for performance evaluation.

Keywords:

brain tumor classification deep learning glioma machine learning meningioma neural networks pituitary supervised classification

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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Main Results:

The best-trained neural network achieved an average cross-validation accuracy of 91.43%.
Deep learning models demonstrated high accuracy in classifying different brain tumor types.
The general deep learning approach outperformed specialized methods in tumor classification.

Conclusions:

Deep learning is a highly effective method for brain tumor classification using MRI data.
This study highlights the potential of DL to improve diagnostic accuracy and potentially patient outcomes.
The findings suggest DL can be a valuable tool in neuro-oncology for automated tumor detection and classification.