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)


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.


AI Machine learning Tissue engineering 


  1. 1.
    Krizhevsky, A., Sutskever, I., Hinton, G.: ImageNet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Systems 25 (2012)Google Scholar
  2. 2.
    Chen, L., Papandreou, G. Kokkinos, I., Murphy, K., Yuille, A.: Semantic image segmentation with deep convolutional nets and fully connected CRFs (2014)Google Scholar
  3. 3.
    Redmon, J., Divvala, S., Girshick, R., Farhadi A.: You only look once: unified, real-time object detection (2015)Google Scholar
  4. 4.
    Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks (2015)Google Scholar
  5. 5.
    Albarqouni, S., Baur, C., Achilles, F., Belagiannis, V., Demirci, S., Navab, N.: AggNet: deep learning from crowds for mitosis detection in breast cancer histology images (2016)Google Scholar
  6. 6.
    Van Grinsven, M., Van Ginneken, B., Hoyng, C., Theelen, T., Sánchez, C.: Fast convolutional neural network training using selective data sampling: application to hemorrhage detection in color fundus images (2016)Google Scholar
  7. 7.
    Yang, L., Zhang, Y., Chen, J., Zhang, S., Chen., D.: Suggestive annotation: a deep active learning framework for biomedical image segmentation (2017)Google Scholar
  8. 8.
    Esteva, A., Kuprel, B., Novoa, R., Ko, J., Swetter, S., Blau, H., Thrun, S.: Dermatologist-level classification of skin cancer with deep neural networks (2017)Google Scholar
  9. 9.
    Fukushima, K.: Neocognitron: a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol. Cybern. 36(4): 193–202. (1980)CrossRefGoogle Scholar
  10. 10.
    Fukushima, K., Miyake, S., Ito, T.: Neocognitron: a neural network model for a mechanism of visual pattern recognition. IEEE Trans. Syst. Man, and Cybern. SMC-13(3), 826–834 (1983)CrossRefGoogle Scholar
  11. 11.
    Fukushima, K.: A hierarchical neural network model for selective attention. In: Eckmiller, R., Von der Malsburg, C. (eds,) Neural Computers, pp. 81–90. Springer-Verlag (1987)Google Scholar
  12. 12.
    Hubel, D., Wiesel, T.: Receptive fields and functional architecture of monkey striate cortex. J. Physiol. 195(1), 215–243 (1968)CrossRefGoogle Scholar
  13. 13.
    LeCun, Y., Boser, B., Denker, J., Henderson, D., Howard, R., Hubbard, W., Jackel, L. Backpropagation applied to handwritten zip code recognition. Neural Comput. (1989)Google Scholar
  14. 14.
    Hotaling, N., Bharti, K., Kriel, H., Simon, C. Diameter, J.: A validated opensource nanofiber diameter measurement tool. Biomaterials 61(August), 327–338 (2015)CrossRefGoogle Scholar
  15. 15.
    Lin, M., Chen, Q., Yan, S.: Network in network (2013)Google Scholar
  16. 16.
    Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting (2014)Google Scholar
  17. 17.
    Hahnloser, R., Sarpeshkar, R., Mahowald, M., Douglas, R., Seung, H.: Digital selection and analogue amplification coexist in a cortex-inspired silicon circuit. Nature 405, 947–951 (2000)CrossRefGoogle Scholar
  18. 18.
    Ramachandran, P., Barret, Z., Quoc, L.: Searching for activation functions (2017)Google Scholar
  19. 19.
    Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization (2014)Google Scholar
  20. 20.
    Hotaling, N., Jeon, J. Wade, M., Luong, D. Palmer, X-L., Bharti, K. Simon Jr, C.: Training to improve precision and accuracy in the measurement of fiber morphology. PLOS One 11, e0167664 (2016)CrossRefGoogle Scholar
  21. 21.
    Chen, D., Sarkar, S., Candia, J., Florczyk, S., Bodhak, S., Driscoll, M., Simon, C., Dunkers, J., Losert, W.: Machine learning based methodology to identify cell shape phenotypes associated with microenvironmental cues. Biomaterials 104, 104–118 (2016)CrossRefGoogle Scholar
  22. 22.
    Patrick, S., Mollica,P., Bruno, R.: Tissue specific microenvironments: a key tool for tissue engineering and regenerative medicine. J. Biol. Eng. 11(1) (2017)Google Scholar

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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