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Title:           iMAGING: a novel automated system for malaria diagnosis by using artificial intelligence tools and a universal low-cost robotized microscope
Subject:         Introduction: Malaria is one of the most prevalent infectious diseases in sub-Saharan Africa, with 247 million cases reported worldwide in 2021 according to the World Health Organization.
Author:          Carles Rubio Maturana; Allisson Dantas de Oliveira; Sergi Nadal; Francesc Zarzuela Serrat; Elena Sulleiro; Edurne Ruiz; Besim Bilalli; Anna Veiga; Mateu Espasa; Alberto Abelló; Tomàs Pumarola Suñé; Marta Segú; Daniel López-Codina; Elisa Sayrol Clols; Joan Joseph-Munné
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     Item: 
      Title: iMAGING: a novel automated system for malaria diagnosis by using artificial intelligence tools and a universal low-cost robotized microscope
      Children: 
       Item: 
        Title: 1 Background
       Item: 
        Title: 2 Materials and methods
        Children: 
         Item: 
          Title: 2.1 Convolutional neural network algorithms
         Item: 
          Title: 2.1.1 Image acquisition
         Item: 
          Title: 2.1.2 Image pre-processing
         Item: 
          Title: 2.1.3 Image annotation
         Item: 
          Title: 2.1.4 Convolutional neural networks training
         Item: 
          Title: 2.1.5 Attention modules
         Item: 
          Title: 2.1.6 Statistical analysis
         Item: 
          Title: 2.2 Microscope automation and smartphone application
         Item: 
          Title: 2.2.1 Design of a 3D-printed prototype for microscopy automation
         Item: 
          Title: 2.2.2 Auto-focus algorithm
         Item: 
          Title: 2.2.3 Integration of trained CNN models into computer software
       Item: 
        Title: 3 Results
        Children: 
         Item: 
          Title: 3.1 Convolutional neural network
         Item: 
          Title: 3.1.1 Malaria thick blood smear image database analysis
         Item: 
          Title: 3.1.2 Convolutional neural network comparison
         Item: 
          Title: 3.1.3 Attention modules comparison
         Item: 
          Title: 3.1.4 YOLOv5x negative sample validation
         Item: 
          Title: 3.1.5 YOLOv5x Low-parasite density validation
         Item: 
          Title: 3.1.6 Automated parasite density calculations
         Item: 
          Title: 3.2 Microscopy automation and smartphone application
         Item: 
          Title: 3.2.1 Universal microscopy automation prototype
         Item: 
          Title: 3.2.2 Autofocus evaluation
         Item: 
          Title: 3.2.3 Imaging app software for malaria diagnosis
       Item: 
        Title: 4 Discussion
       Item: 
        Title: 5 Conclusion
       Item: 
        Title: Data availability statement
       Item: 
        Title: Ethics statement
       Item: 
        Title: Author contributions
     Item: 
      Title: References
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    Author: Carles Rubio Maturana; Allisson Dantas de Oliveira; Sergi Nadal; Francesc Zarzuela Serrat; Elena Sulleiro; Edurne Ruiz; Besim Bilalli; Anna Veiga; Mateu Espasa; Alberto Abelló; Tomàs Pumarola Suñé; Marta Segú; Daniel López-Codina; Elisa Sayrol Clols; Joan Joseph-Munné
    Subject: Introduction: Malaria is one of the most prevalent infectious diseases in sub-Saharan Africa, with 247 million cases reported worldwide in 2021 according to the World Health Organization.
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