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  Published Paper Details:

  Paper Title

LUNG CANCER DETECTION BY USING CNN METHOD: A REVIEW

  Authors

  ANANT AGARWAL,  NANDINI SHARMA

  Keywords

Image Visualization, Fidelity image, CNN

  Abstract


Medical Image Processing Modern three-dimensional (3-D) medical imaging offers the potential and promise for major advances in science and medicine as higher fidelity images are produced. Due to advances in computer aided diagnosis and continuous progress in the field of computerized medical image visualization there is need to develop one of the most important fields within scientific imaging. There are number of types of cancers exist out of that lung cancer is one of the most common cancers. Cancer patients are increasing day by day due to the lifestyle of people. In both man and women lung cancer has become the second most common cancer which causes death. From the early basis report on cancer patients it has been seen that more number of people die of lung cancer than from other cancers such as colon, breast and prostate cancers combined. Lung cancer are related to smoking (or secondhand smoke), or less often to exposure to radon or other environmental factors that�s why this can be prevented. But still it is not yet clear if these cancers can be prevented or not. In this research work, approach of segmentation, feature extraction and CNN will be applied for locating, characterizing cancer portion.

  IJCRT's Publication Details

  Unique Identification Number - IJCRT2006432

  Paper ID - 195759

  Page Number(s) - 3129-3134

  Pubished in - Volume 8 | Issue 6 | June 2020

  DOI (Digital Object Identifier) -   

  Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882

  E-ISSN Number - 2320-2882

  Cite this article

  ANANT AGARWAL,  NANDINI SHARMA,   "LUNG CANCER DETECTION BY USING CNN METHOD: A REVIEW", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.8, Issue 6, pp.3129-3134, June 2020, Available at :http://www.ijcrt.org/papers/IJCRT2006432.pdf

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ISSN: 2320-2882
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ISSN and 7.97 Impact Factor Details


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ISSN: 2320-2882
Impact Factor: 7.97 and ISSN APPROVED
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