@techreport{TR-IC-PFG-20-14,
   number = {IC-PFG-20-14},
   author  =  {Guilherme  Tiaki Sassai Sato and Leodécio Braz da Silva
                   Segundo and Zanoni Dias},
   title  =  {{Classification  of  Musculoskeletal  Abnormalities with
                   Convolutional Neural Networks}},
   month = {August},
   year = {2020},
   institution = {Institute of Computing, University of Campinas},
   note = {In English, 24 pages.
    \par\selectlanguage{english}\textbf{Abstract}
       Computer-aided  diagnosis  has  the  potential to alleviate the
       burden  on  medical  doctors  and  decrease  misdiagnosis,  but 
       building  a  successful  method for automatic classification is
       challenging  due to insufficient labeled data. In this work, we
       investigate  the  usage  of  convolutional  neural  networks to
       diagnose   musculoskeletal   abnormalities   using  radiographs 
       (X-rays)  of  the  upper limb and measure the impact of several
       techniques  in  our  model.  While these techniques are overall
       well-established,  some  did not generalized to out setting. We
       achieved  the  best results by utilizing an ensemble model that
       employs  a  support vector machine to combine different models,
       resulting  in  an overall AUC ROC of 0.8791 and Kappa of 0.6724
       when evaluated using an independent test set.
  }
}