TY  - JOUR
A1  - Bornhorst, Julia
A1  - Nustede, Eike Jannik
A1  - Fudickar, Sebastian
T1  - Mass Surveilance of C. elegans-Smartphone-Based DIY Microscope and Machine-Learning-Based Approach for Worm Detection
JF  - Sensors
N2  - The nematode Caenorhabditis elegans (C. elegans) is often used as an alternative animal model due to several advantages such as morphological changes that can be seen directly under a microscope. Limitations of the model include the usage of expensive and cumbersome microscopes, and restrictions of the comprehensive use of C. elegans for toxicological trials. With the general applicability of the detection of C. elegans from microscope images via machine learning, as well as of smartphone-based microscopes, this article investigates the suitability of smartphone-based microscopy to detect C. elegans in a complete Petri dish. Thereby, the article introduces a smartphone-based microscope (including optics, lighting, and housing) for monitoring C. elegans and the corresponding classification via a trained Histogram of Oriented Gradients (HOG) feature-based Support Vector Machine for the automatic detection of C. elegans. Evaluation showed classification sensitivity of 0.90 and specificity of 0.85, and thereby confirms the general practicability of the chosen approach.
KW  - Caenorhabditis elegans
KW  - machine learning
KW  - smartphone
KW  - microscope
KW  - SVM
KW  - HOG
Y1  - 2019
U6  - https://doi.org/10.3390/s19061468
SN  - 1424-8220
VL  - 19
IS  - 6
PB  - MDPI
CY  - Basel
ER  -