Cite this DOI

Shelke, R., Sutar, T., & Gayakwad, S. (2021). Machine Learning Based Brain Tumor Detection. *Journal of Science & Technology*, *06*(01), 131–136. https://doi.org/10.46243/jst.2021.v6.i04.pp131-136

@article{shelke2021machine,
  author    = {Shelke, Rajshri and Sutar, Tanmay and Gayakwad, Suraj},
  title     = {{Machine Learning Based Brain Tumor Detection}},
  journal   = {Journal of Science \& Technology},
  year      = {2021},
  month     = {aug},
  volume    = {06},
  number    = {01},
  pages     = {131--136},
  publisher = {Longman Publishers},
  issn      = {2456-5660},
  doi       = {10.46243/jst.2021.v6.i04.pp131-136},
  url       = {https://doi.org/10.46243/jst.2021.v6.i04.pp131-136},
  language  = {en},
  abstract  = {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.}
}

TY  - JOUR
TI  - Machine Learning Based Brain Tumor Detection
AU  - Shelke, Rajshri
AU  - Sutar, Tanmay
AU  - Gayakwad, Suraj
JO  - Journal of Science & Technology
PY  - 2021
DA  - 2021/08/16/
VL  - 06
IS  - 01
SP  - 131
EP  - 136
PB  - Longman Publishers
SN  - 2456-5660
LA  - en
AB  - 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.
DO  - 10.46243/jst.2021.v6.i04.pp131-136
UR  - https://doi.org/10.46243/jst.2021.v6.i04.pp131-136
ER  -

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    "URL": "https://doi.org/10.46243/jst.2021.v6.i04.pp131-136",
    "title": "Machine Learning Based Brain Tumor Detection",
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    "author": [
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    "language": "en",
    "abstract": "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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From the record as registered (version 2) — the record and its history. Programs: https://registry.smartscholars.in/api.php?action=cite&doi=10.46243%2Fjst.2021.v6.i04.pp131-136 gives all four in one JSON answer.