Multi-Classification of Brain Tumor Images Using Deep Neural Network

By Hossam H. Sultan, Nancy M. Salem, Walid Al-Atabany. IEEE Access, 2019

552 citations recorded by the source corpus.

Brain tumor classification is a crucial task to evaluate the tumors and make a treatment decision according to their classes. There are many imaging techniques used to detect brain tumors. However, MRI is commonly used due to its superior image quality and the fact of relying on no ionizing radiation. Deep learning (DL) is a subfield of machine learning and recently showed a remarkable performance, especially in classification and segmentation problems. In this paper, a DL model based on a convolutional neural network is proposed to classify different brain tumor types using two publicly available datasets. The former one classifies tumors into (meningioma, glioma, and pituitary tumor). The other one differentiates between the three glioma grades (Grade II, Grade III, and Grade IV). The datasets include 233 and 73 patients with a total of 3064 and 516 images on T1-weighted contrast-enhanced images for the first and second datasets, respectively. The proposed network structure achieves a significant performance with the best overall accuracy of 96.13% and 98.7%, respectively, for the two studies. The results indicate the ability of the model for brain tumor multi-classification purposes.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Neurology

View Paper: https://www.semanticscholar.org/paper/abb9b66bc548dbbf25ae242e0996f8a61027cf23

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Citations in the corpus

A Deep Learning Approach for Brain Tumor Classification and Segmentation Using a Multiscale Convolutional Neural Network (2021, methodology): “[26] proposed a CNN network including 16 convolution layers.”
Classification of Brain Tumors from MRI Images Using a Convolutional Neural Network (2020, background): “[35] 68% data in training set, 32% in validation and test 96.”
A Hybrid CNN-SVM Threshold Segmentation Approach for Tumor Detection and Classification of MRI Brain Images (2021, methodology): “[29], proposed a multi-classification of brain tumor MRI images based on DNN, it tries with two Publicly available datasets to make a new model, that firstly define the tumor type, and in the second step define the glioma grades of detect image.”