Brain Tumor Classification Using Convolutional Neural Networks

Seetha, J.; Raja, S. Selvakumar

doi:10.13005/bpj/1511

Cited by 298 publications

(113 citation statements)

References 5 publications

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“…They used BRATS 2015, 2017, and 2018 datasets for evaluation, and achieved improved classification accuracy. In [ 25 ], the authors presented a CNN-based scheme for the classification of brain tumors. They considered the problem of structural variability of the tumor around the adjacent regions.…”

Section: Related Workmentioning

confidence: 99%

Multimodal Brain Tumor Classification Using Deep Learning and Robust Feature Selection: A Machine Learning Application for Radiologists

et al. 2020

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Manual identification of brain tumors is an error-prone and tedious process for radiologists; therefore, it is crucial to adopt an automated system. The binary classification process, such as malignant or benign is relatively trivial; whereas, the multimodal brain tumors classification (T1, T2, T1CE, and Flair) is a challenging task for radiologists. Here, we present an automated multimodal classification method using deep learning for brain tumor type classification. The proposed method consists of five core steps. In the first step, the linear contrast stretching is employed using edge-based histogram equalization and discrete cosine transform (DCT). In the second step, deep learning feature extraction is performed. By utilizing transfer learning, two pre-trained convolutional neural network (CNN) models, namely VGG16 and VGG19, were used for feature extraction. In the third step, a correntropy-based joint learning approach was implemented along with the extreme learning machine (ELM) for the selection of best features. In the fourth step, the partial least square (PLS)-based robust covariant features were fused in one matrix. The combined matrix was fed to ELM for final classification. The proposed method was validated on the BraTS datasets and an accuracy of 97.8%, 96.9%, 92.5% for BraTs2015, BraTs2017, and BraTs2018, respectively, was achieved.

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Section: Related Workmentioning

confidence: 99%

Multimodal Brain Tumor Classification Using Deep Learning and Robust Feature Selection: A Machine Learning Application for Radiologists

et al. 2020

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“…In these experiments, the comparison of 12 different ensemble algorithms and 11 machine learning classifiers has been presented according to their accuracy. J. Seetha et al [17] proposed brain tumor classification using CNN classifier. In this approach, the FCM algorithm is used to segment out the brain tumor, GLCM used to extract the features while SVM and Deep Neural Network algorithm to classify the features.…”

Section: Literature Surveymentioning

confidence: 99%

Brain MRI Classification using Deep Learning Algorithm

Kulkarni,

Sundari

2020

IJEAT

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The brain tumor is one of the most dangerous, common and aggressive diseases which leads to a very short life expectancy at the highest grade. Thus, to prevent life from such disease, early recognition, and fast treatment is an essential step. In this approach, MRI images are used to analyze brain abnormalities. The manual investigation of brain tumor classification is a time-consuming task and there might have possibilities of human errors. Hence accurate analysis in a tiny span of time is an essential requirement. In this approach, the automatic brain tumor classification algorithm using a highly accurate Convolutional Neural Network (CNN) algorithm is presented. Initially, the brain part is segmented by thresholding approach followed by a morphological operation. The AlexNet transfer learning network of CNN is used because of the limitation of the brain MRI dataset. The classification layer of Alexnet is replaced by the softmax layer with benign and malignant training images and trained using small weights. The experimental analysis demonstrates that the proposed system achieves the F-measure of 98.44% with low complexity than the state-of-arts method.

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“…Although being a highly specific and sensitive method, it is prone to sampling bias, and is time consuming and cost intense. Just recently, research groups have established first convolutional neural networks (CNN) and deep learning algorithms for the non-invasive classification of brain tumors based on MRI [ 17 , 18 ]. Likewise, another group recently reported deep CNNs applied to intraoperative stimulated Raman histology for brain tumor classification [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

Improved Diagnostic Imaging of Brain Tumors by Multimodal Microscopy and Deep Learning

Gesperger

Lichtenegger

Roetzer

et al. 2020

Cancers

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Fluorescence-guided surgery is a state-of-the-art approach for intraoperative imaging during neurosurgical removal of tumor tissue. While the visualization of high-grade gliomas is reliable, lower grade glioma often lack visible fluorescence signals. Here, we present a hybrid prototype combining visible light optical coherence microscopy (OCM) and high-resolution fluorescence imaging for assessment of brain tumor samples acquired by 5-aminolevulinic acid (5-ALA) fluorescence-guided surgery. OCM provides high-resolution information of the inherent tissue scattering and absorption properties of tissue. We here explore quantitative attenuation coefficients derived from volumetric OCM intensity data and quantitative high-resolution 5-ALA fluorescence as potential biomarkers for tissue malignancy including otherwise difficult-to-assess low-grade glioma. We validate our findings against the gold standard histology and use attenuation and fluorescence intensity measures to differentiate between tumor core, infiltrative zone and adjacent brain tissue. Using large field-of-view scans acquired by a near-infrared swept-source optical coherence tomography setup, we provide initial assessments of tumor heterogeneity. Finally, we use cross-sectional OCM images to train a convolutional neural network that discriminates tumor from non-tumor tissue with an accuracy of 97%. Collectively, the present hybrid approach offers potential to translate into an in vivo imaging setup for substantially improved intraoperative guidance of brain tumor surgeries.

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Brain Tumor Classification Using Convolutional Neural Networks

Cited by 298 publications

References 5 publications

Multimodal Brain Tumor Classification Using Deep Learning and Robust Feature Selection: A Machine Learning Application for Radiologists

Multimodal Brain Tumor Classification Using Deep Learning and Robust Feature Selection: A Machine Learning Application for Radiologists

Brain MRI Classification using Deep Learning Algorithm

Improved Diagnostic Imaging of Brain Tumors by Multimodal Microscopy and Deep Learning

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