Chenjie Chang
ABSTRACT
Molecular vibrational spectroscopy is an essential technique for analyzing the biomolecular components of human tissues and body fluids with high sensitivity. There are many studies on tumor disease diagnosis that combine artificial intelligence with vibrational spectroscopy, and vibrational spectroscopy has shown good potential in non-invasive disease auxiliary diagnosis. However, the complexity of spectral data processing flow and the variety of algorithms make it difficult for non-professionals to use it efficiently, which limits the development of molecular vibrational spectroscopy-assisted diagnosis technology. This study developed the Spectral Data Processing Platform (SDPP) that normalizes the data processing flow. SDPP can quickly perform preprocessing, feature extraction, and model building on spectral data. As an application desktop program, SDPP has an executable file size of 232 M, an average response time of 3 s for each component, an average CPU utilization rate of 7.30%, and a throughput of 106 times/s. The experimental data selected for testing the platform were collected from Raman data and infrared data of patients and the control group. The results showed that SDPP could efficiently classify patients and the control group and also proved the stability and operability of SDPP.
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Index Terms
- SDPP: A tumor disease diagnosis application based on vibration spectroscopy and machine learning
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