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ScaffoldNet: Detecting and Classifying Biomedical Polymer-Based Scaffolds via a Convolutional Neural Network

  • Darlington Ahiale AkogoEmail author
  • Xavier-Lewis Palmer
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 70)

Abstract

We developed a Convolutional Neural Network model to identify and classify Airbrushed (alternatively known as Blow-spun), Electrospun and Steel Wire scaffolds. Our model ScaffoldNet is a 6-layer Convolutional Neural Network trained and tested on 3043 images of Airbrushed, Electrospun and Steel Wire scaffolds. The model takes in as input an imaged scaffold and then outputs the scaffold type (Airbrushed, Electrospun or Steel Wire) as predicted probabilities for the 3 classes. Our model scored a 99.44% Accuracy, demonstrating potential for adaptation to investigating and solving complex machine learning problems aimed at abstract spatial contexts, or in screening complex, biological, fibrous structures seen in cortical bone and fibrous shells.

Keywords

AI Machine learning Tissue engineering 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Darlington Ahiale Akogo
    • 1
    Email author
  • Xavier-Lewis Palmer
    • 1
    • 2
  1. 1.MinoHealth AI LabsAccraGhana
  2. 2.Biomedical Engineering InstituteOld Dominion UniversityNorfolkUSA

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