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

TL;DR

This paper introduces ScaffoldNet, a 6-layer CNN that accurately classifies biomedical polymer-based scaffolds from images, achieving over 99% accuracy, with potential applications in biomedical imaging and material analysis.

Contribution

The paper presents a novel CNN model, ScaffoldNet, specifically designed for classifying different types of biomedical scaffolds with high accuracy.

Findings

ScaffoldNet achieved 99.44% accuracy on scaffold classification.

The model effectively distinguishes between Airbrushed, Electrospun, and Steel Wire scaffolds.

Potential applications include biomedical imaging and complex structure analysis.

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 3,043 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.