Rapid Detection of Bacteria Using Raman Spectroscopy and Deep Learning
| Autor: | Kaitlyn Kukula, Christie Chatterley, Nishatul Majid, Denzel Farmer, Yiyan Li, Jeff Jessing, Jesse Duran |
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| Rok vydání: | 2021 |
| Předmět: |
0301 basic medicine
Network complexity biology business.industry Computer science Deep learning Pattern recognition 02 engineering and technology 021001 nanoscience & nanotechnology biology.organism_classification Rapid detection Convolutional neural network 03 medical and health sciences Identification (information) symbols.namesake 030104 developmental biology symbols Artificial intelligence 0210 nano-technology business Raman spectroscopy Bacteria Raman scattering |
| Zdroj: | CCWC |
| DOI: | 10.1109/ccwc51732.2021.9375955 |
| Popis: | Bacteria identification can be a time-consuming process. Machine learning algorithms that use deep convolutional neural networks (CNNs) provide a promising alternative. Here, we present a deep learning based approach paired with Raman spectroscopy to rapidly and accurately detect the identity of a bacteria class. We propose a simple 4-layer CNN architecture and use a 30-class bacteria isolate dataset for training and testing. We achieve an identification accuracy of around 86% with identification speeds close to real-time. This optical/biological detection method is promising for applications in the detection of microbes in liquid biopsies and concentrated environmental liquid samples, where fast and accurate detection is crucial. This study uses a recently published dataset of Raman spectra from bacteria samples and an improved CNN model built with TensorFlow. Results show improved identification accuracy and reduced network complexity. |
| Databáze: | OpenAIRE |
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