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            "value": "The brain tumors, are the most common and aggressive disease and it is challenging task to detect brain tumor in early stages of life, it leads to a very short life expectancy in their highest grade. Thus, treatment planning will be a key stage to improve the quality of life of patients. To evaluate the tumor in a brain used various image techniques such as Computed Tomography (CT), Magnetic Resonance Imaging (MRI) and ultrasound image etc. Mostly, in this work MRI images are used to diagnose tumor in the brain. The huge amount of data generated by MRI scan that helps to classify tumor vs non-tumor in a particular time. But it having some limitation (i.e.) accurate quantitative measurements will be provided for limited number of images. To prevent death rate of human trusted and automatic classification scheme are essential. The automatic brain tumor classification will be very challenging task in large spatial and structural variability of surrounding region of brain tumor. In this work, automatic brain tumor detection will be proposed by using Convolutional Neural Networks (CNN) classification. The deeper architecture design will be performed by using small kernels. The weight of the neuron will be given as small.",
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                "unstructured": "Ghaith Husani, Omar Darwish, et al,” Machine learning approach for Brain tumor detection”..Kimmi Verna et al,” Image processing techniques for the enhancement of brain tumor patterns ”,international"
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                "unstructured": "journal of advanced research in electrical, electronics and instrumentation engineering, Apr 2013"
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                "unstructured": "Harshini Badisa et al. “CNN based brain tumor detection ”,international journal of engineering and advanced technology, Apr 2019. [5].J Seetha et al,” Brain tumor classification using CNN ”,Biomedical and pharmacology journal,Sep 2018 [6].Dena Nadir George. M et al. “Brain tumor detection using shape features and machine learning algorithm”, international journal of scientific and engineering research, Dec 2015"
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