An efficient optimization system for early breast cancer diagnosis based on internet of medical things and deep learning

HIGHLIGHTS

• What: This study proposes an approach to improve and refine the selection of important features that can detect tumors early using Machine Learning (ML) techniques and optimizing the hyperparameters of a Convolutional Neural_Network (CNN) model to achieve better classification results. This study attempted to devise a method to help patients determine their own risk for the disease at an early stage. This study showed that this method was useful and achieved better results than commonly used baselines on the following benchmark datasets: MIAS, INBreast, BCDR, and CBIS-DDSM .
• Who: gerry from the Department of Computer . . .
  

   

  

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