A practical convolutional neural network model for discriminating Raman spectra of human and animal blood

• Published in: Journal of chemometrics Vol. 33; no. 11
• Main Authors: Dong, Jialin, Hong, Mingjian, Xu, Yi, Zheng, Xiangquan
• Format: Journal Article
• Language: English
• Published: Chichester Wiley Subscription Services, Inc 01-11-2019
• Subjects: Artificial neural networks; Background noise; Blood; blood discrimination; convolutional neural networks; Neural networks; Noise reduction; Preprocessing; Raman spectra; Raman spectrum; spectroscopy
• ISSN: 0886-9383; 1099-128X
• DOI: 10.1002/cem.3184

A practical convolutional neural network (CNN) model is proposed to discriminate the Raman spectra of human and animal blood. The proposed network, which discards the pooling layers to avoid loss of data, consists of preprocessing and fully connected classifier layers. Two preprocessing layers, namely, denoising and baseline correction layer, are designed to allow only one kernel for each layer to explicitly suppress the noise and subtract varying background of the spectra. The network combines the preprocessing and discrimination to form a whole processing unit and learns parameters adaptively by training from 217 of 326 Raman spectra of human, dog, and rabbit blood samples. The trained network is evaluated by remaining 109 samples and shows better classification accuracy, as compared with the PLSDA and SVM.
A practical convolutional neural network is proposed to discriminate the Raman spectra of human and animal blood. The network combines the preprocessing and discrimination to form a whole processing unit and learns parameters adaptively by training. Two preprocessing layers, namely, denoising and baseline correction layer, are designed to allow only one kernel for each layer to explicitly suppress the noise and subtract varying background of the spectra.