Deep neural network with generative adversarial networks pre-training for brain tumor classification based on MR images

By Navid Ghassemi, A. Shoeibi, M. Rouhani. Biomedical Signal Processing and Control, 2020

374 citations recorded by the source corpus.

Abstract In this paper, a new deep learning method for tumor classification in MR images is presented. A deep neural network is first pre-trained as a discriminator in a generative adversarial network (GAN) on different datasets of MR images to extract robust features and to learn the structure of MR images in its convolutional layers. Then the fully connected layers are replaced and the whole deep network is trained as a classifier to distinguish three tumor classes. The deep neural network classifier has six layers and about 1.7 million weight parameters. Pre-training as a discriminator of a GAN together with other techniques such as data augmentations (image rotation and mirroring) and dropout prevent the network from overtraining on a relatively small dataset. This method is applied to an MRI data set consists of 3064 T1-CE MR images from 233 patients, 13 images from each patient on average, with three different brain tumor types: meningioma (708 images), glioma (1426 images), and pituitary tumor (930 images). 5-Fold cross-validation is used to evaluate the performance of overall design, achieving the highest accuracy as compared to state-of-art methods.

Subfields: Artificial Intelligence, Biophysics, Neurology

View Paper: https://www.semanticscholar.org/paper/5d0abcbe11275a93c2fa1e896d06effa8d01c786

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In Artificial Intelligence

Citations in the corpus

Epileptic Seizures Detection Using Deep Learning Techniques: A Review (2020, background): “2D - CONVOLUTIONAL NEURAL NETWORKS Nowadays, deep 2D networks are applied to resolve a wide range of computer vision obstacles such as image segmentation [28], medical image classification [29], and face recognition [30].”
COVIDX-Net: A Framework of Deep Learning Classifiers to Diagnose COVID-19 in X-Ray Images (2020, background): “Now, artificial intelligence techniques such as machine learning and deep learning become the core of advanced CAD systems in many medical applications; for example, pulmonary diseases [21, 22], cardiology [23, 24], and brain surgery [25, 26].”