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@article{154190,
        author = {Aditya Nair and Akshay Mohan and Jith James Fernandez and Sabin Biju Seema and Sreena V G},
        title = {Chest X-ray Image Classification for Tuberculosis using Deep Convolutional  Neural  Network},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {8},
        number = {7},
        pages = {96-103},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=154190},
        abstract = {Lung infections are severe health conditions that seriously    affect   the   life   of   human,    especially   those   like Tuberculosis  (TB) which accounts for most of deaths yearly. Therefore,  proper diagnosis  of TB is very much  essential.  Chest X-Rays  (CXR)  are  mostly  been  used  by  medical  industry   for detecting   tuberculosis   as  well  as  other   lung   diseases.   Deep learning based disease identification  systems are in use nowadays, one  among  such  a  deep  learning  architecture is Convolutional Neural   Network   (CNN).   In   this   work,   a   prediction    model using  Deep  Convolutional  Neural   Networks   is  proposed   for detecting TB from input Chest X-ray image. Evaluation of model performance was done  using confusion  matrix  and  accuracy  as metrics. The developed custom network  is paired  with adam optimizer,   which  was  able  to  precisely   differentiate  between normal   and   TB  infected   CXRs.   Finally   an   interactive   web application was built  to deploy the model so that  it can be used to make  predictions on Chest  X-rays  as input.},
        keywords = {Tuberculosis    classification,    Chest     X-Ray,
Convolutional Neural  network,  Deep learning.
},
        month = {},
        }