Multi-grade brain tumor classification using deep CNN with extensive data augmentation

By Muhammad Sajjad, Salman Khan, Khan Muhammad, Wanqing Wu, Amin Ullah, Sung Wook Baik. Journal of Computer Science, 2019

782 citations recorded by the source corpus.

Abstract Numerous computer-aided diagnosis (CAD) systems have been recently presented in the history of medical imaging to assist radiologists about their patients. For full assistance of radiologists and better analysis of magnetic resonance imaging (MRI), multi-grade classification of brain tumor is an essential procedure. In this paper, we propose a novel convolutional neural network (CNN) based multi-grade brain tumor classification system. Firstly, tumor regions from an MR image are segmented using a deep learning technique. Secondly, extensive data augmentation is employed to effectively train the proposed system, avoiding the lack of data problem when dealing with MRI for multi-grade brain tumor classification. Finally, a pre-trained CNN model is fine-tuned using augmented data for brain tumor grade classification. The proposed system is experimentally evaluated on both augmented and original data and results show its convincing performance compared to existing methods.

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

View Paper: https://www.semanticscholar.org/paper/5038eabefad5cfaa606d3085c1654f697fb509fb

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Citations in the corpus

Label-Only Membership Inference Attacks (2020, background): “Augmentations are especially important in low-data regimes [32, 33, 34].”
Deep Multi-Scale 3D Convolutional Neural Network (CNN) for MRI Gliomas Brain Tumor Classification (2020, background): “A variety of data augmentation strategies have been proposed in the literature [38] for deep learning in medical imaging such as random crops, rotation, shears, and flipping.”
Deep Learning for Multigrade Brain Tumor Classification in Smart Healthcare Systems: A Prospective Survey (2020, methodology): “The information about data augmentation and other necessary details are given in [32].”
A Deep Learning Model Based on Concatenation Approach for the Diagnosis of Brain Tumor (2020, background): “[7] proposed a system based on a neural network classification that further aided a clear provision of image segments (segmenting tumor region from dataset).”
Multimodal Brain Tumor Classification Using Deep Learning and Robust Feature Selection: A Machine Learning Application for Radiologists (2020, methodology): “[23] presented a deep learning model and evaluated this on two datasets—Brain tumor and Radiopaedia.”
Classification of Brain Tumors from MRI Images Using a Convolutional Neural Network (2020, background): “In the literature, there are also studies that used the same database for classification with pre-trained networks [23,35–40] or, as input, they use only tumor region or some features that are extracted from the tumor region [7,21,23,41,42].”
Accurate brain tumor detection using deep convolutional neural network (2022, methodology): “, used a fine-tuned VGG19 model for multiclass brain tumor detection and conducted it on 121 images [39].”