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Machine learning in biomedical engineering

  • Received : 2018.01.19
  • Accepted : 2018.02.22
  • Published : 2018.02.28

Abstract

Keywords

References

  1. Samuel A. Some studies in machine learning using the game of checkers. IBM J Res Dev. 1959. https://doi.org/10.1147/rd.33.0210.
  2. http://www.britannica.com/EBchecked/topic/1116194/machinelearning.
  3. Kohavi R, Provost F. Glossary of terms. Mach Learn. 1998;30:271-4. https://doi.org/10.1023/A:1017181826899
  4. Sajid I, Khan UG, Saba T, Rehman A. Computer-assisted brain tumor type discrimination using magnetic resonance imaging features. Biomed Eng Lett. 2018.
  5. Lahmiri S, Dawson DA, Shmuel A. Performance of machine learning methods in diagnosing Parkinson's disease based on dysphonia measures. Biomed Eng Lett. 2018.
  6. Mansour RF. Deep-learning-based automatic computer-aided diagnosis system for diabetic retinopathy. Biomed Eng Lett. 2018.
  7. Relan D, Relan R. Multiscale self-quotient filtering for an improved unsupervised retinal blood vessels characterisations. Biomed Eng Lett. 2018.
  8. Billah M, Washeed S. Gastrointestinal polyp detection in endoscopic images using an improved feature extraction method. Biomed Eng Lett. 2018.
  9. Beritelli F, Capizzi G, Sciuto GL, Napoliy C, Scaglione F. Automatic heart activity diagnosis based on gram polynomials and probabilistic neural networks. Biomed Eng Lett. 2018.
  10. Wei R, Zhang X, Wang J, Dang X. The research of sleep staging based on single-lead electrocardiogram and deep neural network. Biomed Eng Lett. 2018.
  11. Dey D, Chaudhuri S, Munshi S. Obstructive sleep apnoea detection using convolutional neural network based deep learning framework. Biomed Eng Lett. 2018.

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