Journal article Open Access

Brain Tumor Segmentation and Classification using Multiple Feature Extraction and Convolutional Neural Networks

Tasmiya Tazeen; Mrinal Sarvagya


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        <foaf:name>Mrinal Sarvagya</foaf:name>
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    <dct:title>Brain Tumor Segmentation and Classification using Multiple Feature Extraction and Convolutional Neural Networks</dct:title>
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    <dcat:keyword>Segmentation, Brain Tumor, Convolutional Neural Network, Deep Learning.</dcat:keyword>
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    <dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#date">2021-08-30</dct:issued>
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    <dct:description>&lt;p&gt;Intracranial tumors are a type of cancer that grows spontaneously inside the skull. Brain tumor is the cause for one in four deaths. Hence early detection of the tumor is important. For this aim, a variety of segmentation techniques are available. The fundamental disadvantage of present approaches is their low segmentation accuracy. With the help of magnetic resonance imaging (MRI), a preventive medical step of early detection and evaluation of brain tumor is done. Magnetic resonance imaging (MRI) offers detailed information on human delicate tissue, which aids in the diagnosis of a brain tumor. The proposed method in this paper is Brain Tumour Detection and Classification based on Ensembled Feature extraction and classification using CNN.&lt;/p&gt;</dct:description>
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