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Future of Information and Communication Conference

FICC 2019: Advances in Information and Communication pp 152–161Cite as

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

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Part of the book series: Lecture Notes in Networks and Systems ((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.

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Author information

Authors and Affiliations

  1. MinoHealth AI Labs, 00233, SCC Inside, Weija, Accra, Ghana

    Darlington Ahiale Akogo & Xavier-Lewis Palmer

  2. Biomedical Engineering Institute, Old Dominion University, Norfolk, VA23529, USA

    Xavier-Lewis Palmer

Authors
  1. Darlington Ahiale Akogo
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  2. Xavier-Lewis Palmer
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Corresponding author

Correspondence to Darlington Ahiale Akogo .

Editor information

Editors and Affiliations

  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

  2. The Science and Information (SAI) Organization, Bradford, UK

    Rahul Bhatia

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Akogo, D.A., Palmer, XL. (2020). ScaffoldNet: Detecting and Classifying Biomedical Polymer-Based Scaffolds via a Convolutional Neural Network. In: Arai, K., Bhatia, R. (eds) Advances in Information and Communication. FICC 2019. Lecture Notes in Networks and Systems, vol 70. Springer, Cham. https://doi.org/10.1007/978-3-030-12385-7_13

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