Machine learning techniques on homological persistence features for prostate cancer diagnosis

HIGHLIGHTS

• who: Abbas Rammal from the of Science, Lebanese University, u00c9cole des Mines de SaintEtienne, Saintu2011u00c9tienne, France have published the article: Machine learning techniques on homological persistence features for prostate cancer diagnosis, in the Journal: (JOURNAL)
• what: The authors propose to study the Gleason score on some glands issued from a new optical microscopy technique called SLIM. The authors focus on the application of persistent homology, one of the most widely studied and applied TDA tools. One subset is used to validate the model, one subset is used to testing the model, training the model using . . .
  

   

  

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