Noninvasive Grading of Glioma Tumor Using Magnetic Resonance Imaging with Convolutional Neural Networks
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TLDR
A novel approach that uses ConvNet for classifying brain medical images into healthy and unhealthy brain images, using the modified version of the Alex Krizhevsky network (AlexNet) deep learning architecture on magnetic resonance images as a potential tumor classification technique.Abstract:
In recent years, Convolutional Neural Networks (ConvNets) have rapidly emerged as a widespread machine learning technique in a number of applications especially in the area of medical image classification and segmentation. In this paper, we propose a novel approach that uses ConvNet for classifying brain medical images into healthy and unhealthy brain images. The unhealthy images of brain tumors are categorized also into low grades and high grades. In particular, we use the modified version of the Alex Krizhevsky network (AlexNet) deep learning architecture on magnetic resonance images as a potential tumor classification technique. The classification is performed on the whole image where the labels in the training set are at the image level rather than the pixel level. The results showed a reasonable performance in characterizing the brain medical images with an accuracy of 91.16%.read more
Citations
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Magnetic resonance imaging-based brain tumor grades classification and grading via convolutional neural networks and genetic algorithms
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Brain tumor classification in MRI image using convolutional neural network.
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MRI-Based Brain Tumor Classification Using Ensemble of Deep Features and Machine Learning Classifiers
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Multiclass magnetic resonance imaging brain tumor classification using artificial intelligence paradigm.
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