Deep Learning for Brain Tumor Classification

By Justin S. Paul, Andrew J. Plassard, Bennett A. Landman, Daniel Fabbri. Medical Imaging, 2017

329 citations recorded by the source corpus.

Recent research has shown that deep learning methods have performed well on supervised machine learning, image classification tasks. The purpose of this study is to apply deep learning methods to classify brain images with different tumor types: meningioma, glioma, and pituitary. A dataset was publicly released containing 3,064 T1-weighted contrast enhanced MRI (CE-MRI) brain images from 233 patients with either meningioma, glioma, or pituitary tumors split across axial, coronal, or sagittal planes. This research focuses on the 989 axial images from 191 patients in order to avoid confusing the neural networks with three different planes containing the same diagnosis. Two types of neural networks were used in classification: fully connected and convolutional neural networks. Within these two categories, further tests were computed via the augmentation of the original 512×512 axial images. Training neural networks over the axial data has proven to be accurate in its classifications with an average five-fold cross validation of 91.43% on the best trained neural network. This result demonstrates that a more general method (i.e. deep learning) can outperform specialized methods that require image dilation and ring-forming subregions on tumors.

Subfields: Biophysics, Computer Vision and Pattern Recognition, Neurology

View Paper: https://www.semanticscholar.org/paper/33c6a41c1c163856c7fa74e52b6ee24eb7a8bf4f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biophysics

In Computer Vision and Pattern Recognition

In Neurology

Citations in the corpus

A Hybrid Feature Extraction Method With Regularized Extreme Learning Machine for Brain Tumor Classification (2019, background): “In [12-14], the authors introduce a CNN architecture to classify brain tumors.”
Capsule Networks for Brain Tumor Classification Based on MRI Images and Coarse Tumor Boundaries (2018, methodology): “The architecture of the CNN proposed in [27] is as follows:”
Deep Learning for Multigrade Brain Tumor Classification in Smart Healthcare Systems: A Prospective Survey (2020, background): “[53] presented two types of neural networks for the classification of brain tumors with data augmentation to improve the performance of their method.”
MRI-Based Brain Tumor Classification Using Ensemble of Deep Features and Machine Learning Classifiers (2021, methodology): “Since the last decade, deep learning methods have been widely used for brain MRI classiﬁcation [23,24].”
MRI-based brain tumor detection using convolutional deep learning methods and chosen machine learning techniques (2023, methodology): “[25] used deep learning methods to classify brain images related to meningioma, glioma, and pituitary tumors.”
Brain Tumor Type Classification via Capsule Networks (2018, methodology): “Utilization of CNNs for brain tumor type classiﬁcation is recently explored in Reference [17], where neural networks and CNNs are used together with different pre-processing steps including data augmentation.”
Multi-Classification of Brain Tumor Images Using Deep Neural Network (2019, methodology): “In [25], the authors have used only two convolutional layers with 64 kernels in each one.”