Low-cost smartphone-based LIBS combined with deep learning image processing for accurate lithology recognition.
Xu WangSha ChenMengfan WuRuiqin ZhengZhuo LiuZhongjun ZhaoYixiang DuanPublished in: Chemical communications (Cambridge, England) (2021)
A low-cost and multi-channel smartphone-based spectrometer was developed for LIBS. As the CMOS detector is two-dimensional, simultaneous multichannel detection was achieved by coupling a linear array of fibres for light collection. Thus, besides the atomic information, the spectral images containing the propagation and spatial distribution characters of a laser induced plasma plume could be recorded. With these additional features, accurate rock type prediction was achieved by processing the raw data directly through a deep learning model.
Keyphrases
- low cost
- deep learning
- high resolution
- convolutional neural network
- artificial intelligence
- machine learning
- big data
- optical coherence tomography
- electronic health record
- high throughput
- loop mediated isothermal amplification
- mass spectrometry
- real time pcr
- magnetic resonance imaging
- healthcare
- computed tomography
- health information
- magnetic resonance
- image quality
- high density
- contrast enhanced