Abstract

Blood analysis is an indispensable means of detection in criminal investigation, customs security and quarantine, anti-poaching of wildlife, and other incidents. Detecting the species of blood is one of the most important analyses. In order to classify species by analyzing Raman spectra of blood, a recognition method based on deep learning principle is proposed in this paper. This method can realize multi-identification blood species, by constructing a one-dimensional convolution neural network and establishing a Raman spectra database containing 20 kinds of blood. The network model is obtained through training, and then is employed to predict the testing set data. The average accuracy of blind detection is more than 97%. In this paper, we try to increase the diversity of data to improve the robustness of the model, optimize the network and adjust the hyperparameters to improve the recognition ability of the model. The evaluation results show that the deep learning model has high recognition performance to distinguish the species of blood.

© 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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    [Crossref]
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    [Crossref]
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    [Crossref]
  5. H. Yang, B. Zhou, M. Prinz, D. Siegel, and H. Deng, “Body fluid identification by mass spectrometry,” Int. J. Legal Med. 127(6), 1065–1077 (2013).
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  6. E. Sauer, A. K. Reinke, and C. Courts, “Differentiation of five body fluids from forensic samples by expression analysis of four micrornas using quantitative pcr,” Forensic Sci. Int.: Genet. 22, 89–99 (2016).
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  7. W. K. De, L. Lepot, F. Gason, and B. Gilbert, “In search of blood–detection of minute particles using spectroscopic methods,” Forensic Sci. Int. 180(1), 37–42 (2008).
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  9. E. Mistek and I. K. Lednev, “Identification of species’ blood by attenuated total reflection (ATR) Fourier transform infrared (FT-Ir) spectroscopy,” Anal. Bioanal. Chem. 407(24), 7435–7442 (2015).
    [Crossref]
  10. G. Mclaughlin, K. C. Doty, and I. K. Lednev, “Discrimination of human and animal blood traces via raman spectroscopy,” Forensic Sci. Int. 238, 91–95 (2014).
    [Crossref]
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    [Crossref]
  13. E. Mistek, L. Halámková, K. C. Doty, C. K. Muro, and I. K. Lednev, “Race differentiation by raman spectroscopy of a bloodstain for forensic purposes,” Anal. Chem. 88(15), 7453–7456 (2016).
    [Crossref]
  14. A. Sikirzhytskaya, V. Sikirzhytski, and I. K. Lednev, “Determining gender by Raman spectroscopy of a bloodstain,” Anal. Chem. 89(3), 1486–1492 (2017).
    [Crossref]
  15. K. C. Doty and I. K. Lednev, “Differentiating donor age groups based on raman spectroscopy of bloodstains for forensic purposes,” ACS Cent. Sci. 4(7), 862–867 (2018).
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    [Crossref]
  17. K. C. Doty and I. K. Lednev, “Differentiation of human blood from animal blood using raman spectroscopy: A survey of forensically relevant species,” Forensic Sci. Int. 282, 204–210 (2018).
    [Crossref]
  18. H. Bian and J. Gao, “Error analysis of the spectral shift for partial least squares models in raman spectroscopy,” Opt. Express 26(7), 8016–8027 (2018).
    [Crossref]
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    [Crossref]
  20. H. Bian, Y. Zhang, W. Gao, and J. Gao, “Fourier based partial least squares algorithm: New insight into influence of spectral shift in “frequency domain”,” Opt. Express 27(3), 2926–2936 (2019).
    [Crossref]
  21. P. Bai, J. Wang, H. Yin, Y. Tian, W. Yao, and G. Jing, “Discrimination of human and nonhuman blood by raman spectroscopy and partial least squares discriminant analysis,” Anal. Lett. 50(2), 379–388 (2017).
    [Crossref]
  22. G. E. Hinton, S. Osindero, and Y. W. Teh, “A fast learning algorithm for deep belief nets,” Neural Comput. 18(7), 1527–1554 (2006).
    [Crossref]
  23. C. L. Liu, W. H. Hsaio, and Y. C. Tu, “Time series classification with multivariate convolutional neural network,” IEEE Trans. Ind. Electron. 66(6), 4788–4797 (2019).
    [Crossref]
  24. S. Lawrence, C. L. Giles, A. C. Tsoi, and A. D. Back, “Face recognition: A convolutional neural-network approach,” IEEE Trans. Neural Netw. Learn. Syst. 8(1), 98–113 (1997).
    [Crossref]
  25. K. Tsiouris, V. C. Pezoulas, M. Zervakis, S. Konitsiotis, D. D. Koutsouris, and D. I. Fotiadis, “A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals,” Comput. Biol. Med. 99, 24–37 (2018).
    [Crossref]
  26. T. Kooi, G. Litjens, G. B. Van, A. Gubern-Mérida, C. I. Sánchez, R. Mann, H. A. Den, and N. Karssemeijer, “Large scale deep learning for computer aided detection of mammographic lesions,” Med. Image Anal. 35, 303–312 (2017).
    [Crossref]
  27. A. J. Bekker, M. Shalhon, H. Greenspan, and J. Goldberger, “Multi-view probabilistic classification of breast microcalcifications,” IEEE T. Med. Imaging 35(2), 645–653 (2016).
    [Crossref]
  28. A. A. A. Setio, F. Ciompi, G. Litjens, P. Gerke, C. Jacobs, S. J. V. Riel, M. M. W. Wille, M. Naqibullah, C. I. Sánchez, and B. V. Ginneken, “Pulmonary nodule detection in CT images: False positive reduction using multi-view convolutional networks,” IEEE T. Med. Imaging 35(5), 1160–1169 (2016).
    [Crossref]
  29. Q. Dou, H. Chen, L. Yu, J. Qin, and P. A. Heng, “Multi-level contextual 3d cnns for false positive reduction in pulmonary nodule detection,” IEEE Trans. Biomed. Eng. 64(7), 1558–1567 (2017).
    [Crossref]
  30. L. Zhang, Y. Wu, B. Zheng, L. Su, Y. Chen, S. Ma, Q. Hu, X. Zou, L. Yao, and Y. Yang, “Rapid histology of laryngeal squamous cell carcinoma with deep-learning based stimulated raman scattering microscopy,” Theranostics 9(9), 2541–2554 (2019).
    [Crossref]
  31. C. G. Atkins, K. Buckley, M. W. Blades, and R. F. Turner, “Raman spectroscopy of blood and blood components,” Appl. Spectrosc. 71(5), 767–793 (2017).
    [Crossref]
  32. B. R. Wood, P. Caspers, G. J. Puppels, S. Pandiancherri, and D. Mcnaughton, “Resonance raman spectroscopy of red blood cells using near-infrared laser excitation,” Anal. Bioanal. Chem. 387(5), 1691–1703 (2007).
    [Crossref]
  33. P. Lemler, W. R. Premasiri, A. Delmonaco, and L. D. Ziegler, “NIR Raman spectra of whole human blood: Effects of laser-induced and in vitro hemoglobin denaturation,” Anal. Bioanal. Chem. 406(1), 193–200 (2014).
    [Crossref]
  34. H. Sato, H. Chiba, H. Tashiro, and Y. Ozaki, “Excitation wavelength-dependent changes in Raman spectra of whole blood and hemoglobin: Comparison of the spectra with 514.5-, 720-, and 1064-nm excitation,” J. Biomed. Opt. 6(3), 366–370 (2001).
    [Crossref]
  35. J. Cohen, “A coefficient of agreement for nominal scales,” Educ. Psychol. Meas. 20(1), 37–46 (1960).
    [Crossref]
  36. T. Fawcett, “An introduction to roc analysis,” Pattern Recognit. Lett. 27(8), 861–874 (2006).
    [Crossref]

2019 (3)

C. L. Liu, W. H. Hsaio, and Y. C. Tu, “Time series classification with multivariate convolutional neural network,” IEEE Trans. Ind. Electron. 66(6), 4788–4797 (2019).
[Crossref]

L. Zhang, Y. Wu, B. Zheng, L. Su, Y. Chen, S. Ma, Q. Hu, X. Zou, L. Yao, and Y. Yang, “Rapid histology of laryngeal squamous cell carcinoma with deep-learning based stimulated raman scattering microscopy,” Theranostics 9(9), 2541–2554 (2019).
[Crossref]

H. Bian, Y. Zhang, W. Gao, and J. Gao, “Fourier based partial least squares algorithm: New insight into influence of spectral shift in “frequency domain”,” Opt. Express 27(3), 2926–2936 (2019).
[Crossref]

2018 (5)

H. Bian and J. Gao, “Error analysis of the spectral shift for partial least squares models in raman spectroscopy,” Opt. Express 26(7), 8016–8027 (2018).
[Crossref]

H. Bian, P. Wang, N. Wang, Y. Tian, P. Bai, H. Jiang, and J. Gao, “Dual-model analysis for improving the discrimination performance of human and nonhuman blood based on raman spectroscopy,” Biomed. Opt. Express 9(8), 3512–3522 (2018).
[Crossref]

K. Tsiouris, V. C. Pezoulas, M. Zervakis, S. Konitsiotis, D. D. Koutsouris, and D. I. Fotiadis, “A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals,” Comput. Biol. Med. 99, 24–37 (2018).
[Crossref]

K. C. Doty and I. K. Lednev, “Differentiating donor age groups based on raman spectroscopy of bloodstains for forensic purposes,” ACS Cent. Sci. 4(7), 862–867 (2018).
[Crossref]

K. C. Doty and I. K. Lednev, “Differentiation of human blood from animal blood using raman spectroscopy: A survey of forensically relevant species,” Forensic Sci. Int. 282, 204–210 (2018).
[Crossref]

2017 (6)

P. Bai, J. Wang, H. Yin, Y. Tian, W. Yao, and G. Jing, “Discrimination of human and nonhuman blood by raman spectroscopy and partial least squares discriminant analysis,” Anal. Lett. 50(2), 379–388 (2017).
[Crossref]

J. Fujihara, Y. Fujita, T. Yamamoto, N. Nishimoto, K. Kimura-Kataoka, S. Kurata, Y. Takinami, T. Yasuda, and H. Takeshita, “Blood identification and discrimination between human and nonhuman blood using portable raman spectroscopy,” Int. J. Legal Med. 131(2), 319–322 (2017).
[Crossref]

T. Kooi, G. Litjens, G. B. Van, A. Gubern-Mérida, C. I. Sánchez, R. Mann, H. A. Den, and N. Karssemeijer, “Large scale deep learning for computer aided detection of mammographic lesions,” Med. Image Anal. 35, 303–312 (2017).
[Crossref]

A. Sikirzhytskaya, V. Sikirzhytski, and I. K. Lednev, “Determining gender by Raman spectroscopy of a bloodstain,” Anal. Chem. 89(3), 1486–1492 (2017).
[Crossref]

Q. Dou, H. Chen, L. Yu, J. Qin, and P. A. Heng, “Multi-level contextual 3d cnns for false positive reduction in pulmonary nodule detection,” IEEE Trans. Biomed. Eng. 64(7), 1558–1567 (2017).
[Crossref]

C. G. Atkins, K. Buckley, M. W. Blades, and R. F. Turner, “Raman spectroscopy of blood and blood components,” Appl. Spectrosc. 71(5), 767–793 (2017).
[Crossref]

2016 (5)

A. J. Bekker, M. Shalhon, H. Greenspan, and J. Goldberger, “Multi-view probabilistic classification of breast microcalcifications,” IEEE T. Med. Imaging 35(2), 645–653 (2016).
[Crossref]

A. A. A. Setio, F. Ciompi, G. Litjens, P. Gerke, C. Jacobs, S. J. V. Riel, M. M. W. Wille, M. Naqibullah, C. I. Sánchez, and B. V. Ginneken, “Pulmonary nodule detection in CT images: False positive reduction using multi-view convolutional networks,” IEEE T. Med. Imaging 35(5), 1160–1169 (2016).
[Crossref]

C. K. Muro, K. C. Doty, L. D. S. Fernandes, and I. K. Lednev, “Forensic body fluid identification and differentiation by raman spectroscopy,” Forensic Chem. 1, 31–38 (2016).
[Crossref]

E. Mistek, L. Halámková, K. C. Doty, C. K. Muro, and I. K. Lednev, “Race differentiation by raman spectroscopy of a bloodstain for forensic purposes,” Anal. Chem. 88(15), 7453–7456 (2016).
[Crossref]

E. Sauer, A. K. Reinke, and C. Courts, “Differentiation of five body fluids from forensic samples by expression analysis of four micrornas using quantitative pcr,” Forensic Sci. Int.: Genet. 22, 89–99 (2016).
[Crossref]

2015 (1)

E. Mistek and I. K. Lednev, “Identification of species’ blood by attenuated total reflection (ATR) Fourier transform infrared (FT-Ir) spectroscopy,” Anal. Bioanal. Chem. 407(24), 7435–7442 (2015).
[Crossref]

2014 (3)

G. Mclaughlin, K. C. Doty, and I. K. Lednev, “Discrimination of human and animal blood traces via raman spectroscopy,” Forensic Sci. Int. 238, 91–95 (2014).
[Crossref]

G. Mclaughlin, K. C. Doty, I. K. Lednev, and A. Chem, “Raman spectroscopy of blood for species identification,” Anal. Chem. 86(23), 11628–11633 (2014).
[Crossref]

P. Lemler, W. R. Premasiri, A. Delmonaco, and L. D. Ziegler, “NIR Raman spectra of whole human blood: Effects of laser-induced and in vitro hemoglobin denaturation,” Anal. Bioanal. Chem. 406(1), 193–200 (2014).
[Crossref]

2013 (1)

H. Yang, B. Zhou, M. Prinz, D. Siegel, and H. Deng, “Body fluid identification by mass spectrometry,” Int. J. Legal Med. 127(6), 1065–1077 (2013).
[Crossref]

2012 (1)

S. C. Renner, D. Neumann, M. Burkart, U. Feit, P. Giere, A. Gröger, A. Paulsch, C. Paulsch, M. Sterz, and K. Vohland, “Import and export of biological samples from tropical countries–considerations and guidelines for research teams,” Org. Divers. Evol. 12(1), 81–98 (2012).
[Crossref]

2009 (1)

V. Kelly, “Blood species identification for forensic purposes using raman spectroscopy combined with advanced statistical analysis,” Anal. Chem. 81(18), 7773–7777 (2009).
[Crossref]

2008 (1)

W. K. De, L. Lepot, F. Gason, and B. Gilbert, “In search of blood–detection of minute particles using spectroscopic methods,” Forensic Sci. Int. 180(1), 37–42 (2008).
[Crossref]

2007 (1)

B. R. Wood, P. Caspers, G. J. Puppels, S. Pandiancherri, and D. Mcnaughton, “Resonance raman spectroscopy of red blood cells using near-infrared laser excitation,” Anal. Bioanal. Chem. 387(5), 1691–1703 (2007).
[Crossref]

2006 (2)

T. Fawcett, “An introduction to roc analysis,” Pattern Recognit. Lett. 27(8), 861–874 (2006).
[Crossref]

G. E. Hinton, S. Osindero, and Y. W. Teh, “A fast learning algorithm for deep belief nets,” Neural Comput. 18(7), 1527–1554 (2006).
[Crossref]

2001 (1)

H. Sato, H. Chiba, H. Tashiro, and Y. Ozaki, “Excitation wavelength-dependent changes in Raman spectra of whole blood and hemoglobin: Comparison of the spectra with 514.5-, 720-, and 1064-nm excitation,” J. Biomed. Opt. 6(3), 366–370 (2001).
[Crossref]

1999 (1)

E. O. Espinoza, N. C. Lindley, K. M. Gordon, J. A. Ekhoff, and M. A. Kirms, “Electrospray ionization mass spectrometric analysis of blood for differentiation of species,” Anal. Biochem. 268(2), 252–261 (1999).
[Crossref]

1997 (2)

J. Andrasko, “The estimation of age of bloodstains by HPLC analysis,” J. Forensic Sci. 42(4), 14171J (1997).
[Crossref]

S. Lawrence, C. L. Giles, A. C. Tsoi, and A. D. Back, “Face recognition: A convolutional neural-network approach,” IEEE Trans. Neural Netw. Learn. Syst. 8(1), 98–113 (1997).
[Crossref]

1990 (1)

H. Inouel, F. Takabe, O. Takenaka, M. Iwasa, and Y. Maeno, “Species identification of blood and bloodstains by high-performance liquid chromatography,” Int. J. Legal Med. 104(1), 9–12 (1990).
[Crossref]

1960 (1)

J. Cohen, “A coefficient of agreement for nominal scales,” Educ. Psychol. Meas. 20(1), 37–46 (1960).
[Crossref]

Andrasko, J.

J. Andrasko, “The estimation of age of bloodstains by HPLC analysis,” J. Forensic Sci. 42(4), 14171J (1997).
[Crossref]

Atkins, C. G.

Back, A. D.

S. Lawrence, C. L. Giles, A. C. Tsoi, and A. D. Back, “Face recognition: A convolutional neural-network approach,” IEEE Trans. Neural Netw. Learn. Syst. 8(1), 98–113 (1997).
[Crossref]

Bai, P.

H. Bian, P. Wang, N. Wang, Y. Tian, P. Bai, H. Jiang, and J. Gao, “Dual-model analysis for improving the discrimination performance of human and nonhuman blood based on raman spectroscopy,” Biomed. Opt. Express 9(8), 3512–3522 (2018).
[Crossref]

P. Bai, J. Wang, H. Yin, Y. Tian, W. Yao, and G. Jing, “Discrimination of human and nonhuman blood by raman spectroscopy and partial least squares discriminant analysis,” Anal. Lett. 50(2), 379–388 (2017).
[Crossref]

Bekker, A. J.

A. J. Bekker, M. Shalhon, H. Greenspan, and J. Goldberger, “Multi-view probabilistic classification of breast microcalcifications,” IEEE T. Med. Imaging 35(2), 645–653 (2016).
[Crossref]

Bian, H.

Blades, M. W.

Buckley, K.

Burkart, M.

S. C. Renner, D. Neumann, M. Burkart, U. Feit, P. Giere, A. Gröger, A. Paulsch, C. Paulsch, M. Sterz, and K. Vohland, “Import and export of biological samples from tropical countries–considerations and guidelines for research teams,” Org. Divers. Evol. 12(1), 81–98 (2012).
[Crossref]

Caspers, P.

B. R. Wood, P. Caspers, G. J. Puppels, S. Pandiancherri, and D. Mcnaughton, “Resonance raman spectroscopy of red blood cells using near-infrared laser excitation,” Anal. Bioanal. Chem. 387(5), 1691–1703 (2007).
[Crossref]

Chem, A.

G. Mclaughlin, K. C. Doty, I. K. Lednev, and A. Chem, “Raman spectroscopy of blood for species identification,” Anal. Chem. 86(23), 11628–11633 (2014).
[Crossref]

Chen, H.

Q. Dou, H. Chen, L. Yu, J. Qin, and P. A. Heng, “Multi-level contextual 3d cnns for false positive reduction in pulmonary nodule detection,” IEEE Trans. Biomed. Eng. 64(7), 1558–1567 (2017).
[Crossref]

Chen, Y.

L. Zhang, Y. Wu, B. Zheng, L. Su, Y. Chen, S. Ma, Q. Hu, X. Zou, L. Yao, and Y. Yang, “Rapid histology of laryngeal squamous cell carcinoma with deep-learning based stimulated raman scattering microscopy,” Theranostics 9(9), 2541–2554 (2019).
[Crossref]

Chiba, H.

H. Sato, H. Chiba, H. Tashiro, and Y. Ozaki, “Excitation wavelength-dependent changes in Raman spectra of whole blood and hemoglobin: Comparison of the spectra with 514.5-, 720-, and 1064-nm excitation,” J. Biomed. Opt. 6(3), 366–370 (2001).
[Crossref]

Ciompi, F.

A. A. A. Setio, F. Ciompi, G. Litjens, P. Gerke, C. Jacobs, S. J. V. Riel, M. M. W. Wille, M. Naqibullah, C. I. Sánchez, and B. V. Ginneken, “Pulmonary nodule detection in CT images: False positive reduction using multi-view convolutional networks,” IEEE T. Med. Imaging 35(5), 1160–1169 (2016).
[Crossref]

Cohen, J.

J. Cohen, “A coefficient of agreement for nominal scales,” Educ. Psychol. Meas. 20(1), 37–46 (1960).
[Crossref]

Courts, C.

E. Sauer, A. K. Reinke, and C. Courts, “Differentiation of five body fluids from forensic samples by expression analysis of four micrornas using quantitative pcr,” Forensic Sci. Int.: Genet. 22, 89–99 (2016).
[Crossref]

De, W. K.

W. K. De, L. Lepot, F. Gason, and B. Gilbert, “In search of blood–detection of minute particles using spectroscopic methods,” Forensic Sci. Int. 180(1), 37–42 (2008).
[Crossref]

Delmonaco, A.

P. Lemler, W. R. Premasiri, A. Delmonaco, and L. D. Ziegler, “NIR Raman spectra of whole human blood: Effects of laser-induced and in vitro hemoglobin denaturation,” Anal. Bioanal. Chem. 406(1), 193–200 (2014).
[Crossref]

Den, H. A.

T. Kooi, G. Litjens, G. B. Van, A. Gubern-Mérida, C. I. Sánchez, R. Mann, H. A. Den, and N. Karssemeijer, “Large scale deep learning for computer aided detection of mammographic lesions,” Med. Image Anal. 35, 303–312 (2017).
[Crossref]

Deng, H.

H. Yang, B. Zhou, M. Prinz, D. Siegel, and H. Deng, “Body fluid identification by mass spectrometry,” Int. J. Legal Med. 127(6), 1065–1077 (2013).
[Crossref]

Doty, K. C.

K. C. Doty and I. K. Lednev, “Differentiation of human blood from animal blood using raman spectroscopy: A survey of forensically relevant species,” Forensic Sci. Int. 282, 204–210 (2018).
[Crossref]

K. C. Doty and I. K. Lednev, “Differentiating donor age groups based on raman spectroscopy of bloodstains for forensic purposes,” ACS Cent. Sci. 4(7), 862–867 (2018).
[Crossref]

C. K. Muro, K. C. Doty, L. D. S. Fernandes, and I. K. Lednev, “Forensic body fluid identification and differentiation by raman spectroscopy,” Forensic Chem. 1, 31–38 (2016).
[Crossref]

E. Mistek, L. Halámková, K. C. Doty, C. K. Muro, and I. K. Lednev, “Race differentiation by raman spectroscopy of a bloodstain for forensic purposes,” Anal. Chem. 88(15), 7453–7456 (2016).
[Crossref]

G. Mclaughlin, K. C. Doty, I. K. Lednev, and A. Chem, “Raman spectroscopy of blood for species identification,” Anal. Chem. 86(23), 11628–11633 (2014).
[Crossref]

G. Mclaughlin, K. C. Doty, and I. K. Lednev, “Discrimination of human and animal blood traces via raman spectroscopy,” Forensic Sci. Int. 238, 91–95 (2014).
[Crossref]

Dou, Q.

Q. Dou, H. Chen, L. Yu, J. Qin, and P. A. Heng, “Multi-level contextual 3d cnns for false positive reduction in pulmonary nodule detection,” IEEE Trans. Biomed. Eng. 64(7), 1558–1567 (2017).
[Crossref]

Ekhoff, J. A.

E. O. Espinoza, N. C. Lindley, K. M. Gordon, J. A. Ekhoff, and M. A. Kirms, “Electrospray ionization mass spectrometric analysis of blood for differentiation of species,” Anal. Biochem. 268(2), 252–261 (1999).
[Crossref]

Espinoza, E. O.

E. O. Espinoza, N. C. Lindley, K. M. Gordon, J. A. Ekhoff, and M. A. Kirms, “Electrospray ionization mass spectrometric analysis of blood for differentiation of species,” Anal. Biochem. 268(2), 252–261 (1999).
[Crossref]

Fawcett, T.

T. Fawcett, “An introduction to roc analysis,” Pattern Recognit. Lett. 27(8), 861–874 (2006).
[Crossref]

Feit, U.

S. C. Renner, D. Neumann, M. Burkart, U. Feit, P. Giere, A. Gröger, A. Paulsch, C. Paulsch, M. Sterz, and K. Vohland, “Import and export of biological samples from tropical countries–considerations and guidelines for research teams,” Org. Divers. Evol. 12(1), 81–98 (2012).
[Crossref]

Fernandes, L. D. S.

C. K. Muro, K. C. Doty, L. D. S. Fernandes, and I. K. Lednev, “Forensic body fluid identification and differentiation by raman spectroscopy,” Forensic Chem. 1, 31–38 (2016).
[Crossref]

Fotiadis, D. I.

K. Tsiouris, V. C. Pezoulas, M. Zervakis, S. Konitsiotis, D. D. Koutsouris, and D. I. Fotiadis, “A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals,” Comput. Biol. Med. 99, 24–37 (2018).
[Crossref]

Fujihara, J.

J. Fujihara, Y. Fujita, T. Yamamoto, N. Nishimoto, K. Kimura-Kataoka, S. Kurata, Y. Takinami, T. Yasuda, and H. Takeshita, “Blood identification and discrimination between human and nonhuman blood using portable raman spectroscopy,” Int. J. Legal Med. 131(2), 319–322 (2017).
[Crossref]

Fujita, Y.

J. Fujihara, Y. Fujita, T. Yamamoto, N. Nishimoto, K. Kimura-Kataoka, S. Kurata, Y. Takinami, T. Yasuda, and H. Takeshita, “Blood identification and discrimination between human and nonhuman blood using portable raman spectroscopy,” Int. J. Legal Med. 131(2), 319–322 (2017).
[Crossref]

Gao, J.

Gao, W.

Gason, F.

W. K. De, L. Lepot, F. Gason, and B. Gilbert, “In search of blood–detection of minute particles using spectroscopic methods,” Forensic Sci. Int. 180(1), 37–42 (2008).
[Crossref]

Gerke, P.

A. A. A. Setio, F. Ciompi, G. Litjens, P. Gerke, C. Jacobs, S. J. V. Riel, M. M. W. Wille, M. Naqibullah, C. I. Sánchez, and B. V. Ginneken, “Pulmonary nodule detection in CT images: False positive reduction using multi-view convolutional networks,” IEEE T. Med. Imaging 35(5), 1160–1169 (2016).
[Crossref]

Giere, P.

S. C. Renner, D. Neumann, M. Burkart, U. Feit, P. Giere, A. Gröger, A. Paulsch, C. Paulsch, M. Sterz, and K. Vohland, “Import and export of biological samples from tropical countries–considerations and guidelines for research teams,” Org. Divers. Evol. 12(1), 81–98 (2012).
[Crossref]

Gilbert, B.

W. K. De, L. Lepot, F. Gason, and B. Gilbert, “In search of blood–detection of minute particles using spectroscopic methods,” Forensic Sci. Int. 180(1), 37–42 (2008).
[Crossref]

Giles, C. L.

S. Lawrence, C. L. Giles, A. C. Tsoi, and A. D. Back, “Face recognition: A convolutional neural-network approach,” IEEE Trans. Neural Netw. Learn. Syst. 8(1), 98–113 (1997).
[Crossref]

Ginneken, B. V.

A. A. A. Setio, F. Ciompi, G. Litjens, P. Gerke, C. Jacobs, S. J. V. Riel, M. M. W. Wille, M. Naqibullah, C. I. Sánchez, and B. V. Ginneken, “Pulmonary nodule detection in CT images: False positive reduction using multi-view convolutional networks,” IEEE T. Med. Imaging 35(5), 1160–1169 (2016).
[Crossref]

Goldberger, J.

A. J. Bekker, M. Shalhon, H. Greenspan, and J. Goldberger, “Multi-view probabilistic classification of breast microcalcifications,” IEEE T. Med. Imaging 35(2), 645–653 (2016).
[Crossref]

Gordon, K. M.

E. O. Espinoza, N. C. Lindley, K. M. Gordon, J. A. Ekhoff, and M. A. Kirms, “Electrospray ionization mass spectrometric analysis of blood for differentiation of species,” Anal. Biochem. 268(2), 252–261 (1999).
[Crossref]

Greenspan, H.

A. J. Bekker, M. Shalhon, H. Greenspan, and J. Goldberger, “Multi-view probabilistic classification of breast microcalcifications,” IEEE T. Med. Imaging 35(2), 645–653 (2016).
[Crossref]

Gröger, A.

S. C. Renner, D. Neumann, M. Burkart, U. Feit, P. Giere, A. Gröger, A. Paulsch, C. Paulsch, M. Sterz, and K. Vohland, “Import and export of biological samples from tropical countries–considerations and guidelines for research teams,” Org. Divers. Evol. 12(1), 81–98 (2012).
[Crossref]

Gubern-Mérida, A.

T. Kooi, G. Litjens, G. B. Van, A. Gubern-Mérida, C. I. Sánchez, R. Mann, H. A. Den, and N. Karssemeijer, “Large scale deep learning for computer aided detection of mammographic lesions,” Med. Image Anal. 35, 303–312 (2017).
[Crossref]

Halámková, L.

E. Mistek, L. Halámková, K. C. Doty, C. K. Muro, and I. K. Lednev, “Race differentiation by raman spectroscopy of a bloodstain for forensic purposes,” Anal. Chem. 88(15), 7453–7456 (2016).
[Crossref]

Heng, P. A.

Q. Dou, H. Chen, L. Yu, J. Qin, and P. A. Heng, “Multi-level contextual 3d cnns for false positive reduction in pulmonary nodule detection,” IEEE Trans. Biomed. Eng. 64(7), 1558–1567 (2017).
[Crossref]

Hinton, G. E.

G. E. Hinton, S. Osindero, and Y. W. Teh, “A fast learning algorithm for deep belief nets,” Neural Comput. 18(7), 1527–1554 (2006).
[Crossref]

Hsaio, W. H.

C. L. Liu, W. H. Hsaio, and Y. C. Tu, “Time series classification with multivariate convolutional neural network,” IEEE Trans. Ind. Electron. 66(6), 4788–4797 (2019).
[Crossref]

Hu, Q.

L. Zhang, Y. Wu, B. Zheng, L. Su, Y. Chen, S. Ma, Q. Hu, X. Zou, L. Yao, and Y. Yang, “Rapid histology of laryngeal squamous cell carcinoma with deep-learning based stimulated raman scattering microscopy,” Theranostics 9(9), 2541–2554 (2019).
[Crossref]

Inouel, H.

H. Inouel, F. Takabe, O. Takenaka, M. Iwasa, and Y. Maeno, “Species identification of blood and bloodstains by high-performance liquid chromatography,” Int. J. Legal Med. 104(1), 9–12 (1990).
[Crossref]

Iwasa, M.

H. Inouel, F. Takabe, O. Takenaka, M. Iwasa, and Y. Maeno, “Species identification of blood and bloodstains by high-performance liquid chromatography,” Int. J. Legal Med. 104(1), 9–12 (1990).
[Crossref]

Jacobs, C.

A. A. A. Setio, F. Ciompi, G. Litjens, P. Gerke, C. Jacobs, S. J. V. Riel, M. M. W. Wille, M. Naqibullah, C. I. Sánchez, and B. V. Ginneken, “Pulmonary nodule detection in CT images: False positive reduction using multi-view convolutional networks,” IEEE T. Med. Imaging 35(5), 1160–1169 (2016).
[Crossref]

Jiang, H.

Jing, G.

P. Bai, J. Wang, H. Yin, Y. Tian, W. Yao, and G. Jing, “Discrimination of human and nonhuman blood by raman spectroscopy and partial least squares discriminant analysis,” Anal. Lett. 50(2), 379–388 (2017).
[Crossref]

Karssemeijer, N.

T. Kooi, G. Litjens, G. B. Van, A. Gubern-Mérida, C. I. Sánchez, R. Mann, H. A. Den, and N. Karssemeijer, “Large scale deep learning for computer aided detection of mammographic lesions,” Med. Image Anal. 35, 303–312 (2017).
[Crossref]

Kelly, V.

V. Kelly, “Blood species identification for forensic purposes using raman spectroscopy combined with advanced statistical analysis,” Anal. Chem. 81(18), 7773–7777 (2009).
[Crossref]

Kimura-Kataoka, K.

J. Fujihara, Y. Fujita, T. Yamamoto, N. Nishimoto, K. Kimura-Kataoka, S. Kurata, Y. Takinami, T. Yasuda, and H. Takeshita, “Blood identification and discrimination between human and nonhuman blood using portable raman spectroscopy,” Int. J. Legal Med. 131(2), 319–322 (2017).
[Crossref]

Kirms, M. A.

E. O. Espinoza, N. C. Lindley, K. M. Gordon, J. A. Ekhoff, and M. A. Kirms, “Electrospray ionization mass spectrometric analysis of blood for differentiation of species,” Anal. Biochem. 268(2), 252–261 (1999).
[Crossref]

Konitsiotis, S.

K. Tsiouris, V. C. Pezoulas, M. Zervakis, S. Konitsiotis, D. D. Koutsouris, and D. I. Fotiadis, “A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals,” Comput. Biol. Med. 99, 24–37 (2018).
[Crossref]

Kooi, T.

T. Kooi, G. Litjens, G. B. Van, A. Gubern-Mérida, C. I. Sánchez, R. Mann, H. A. Den, and N. Karssemeijer, “Large scale deep learning for computer aided detection of mammographic lesions,” Med. Image Anal. 35, 303–312 (2017).
[Crossref]

Koutsouris, D. D.

K. Tsiouris, V. C. Pezoulas, M. Zervakis, S. Konitsiotis, D. D. Koutsouris, and D. I. Fotiadis, “A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals,” Comput. Biol. Med. 99, 24–37 (2018).
[Crossref]

Kurata, S.

J. Fujihara, Y. Fujita, T. Yamamoto, N. Nishimoto, K. Kimura-Kataoka, S. Kurata, Y. Takinami, T. Yasuda, and H. Takeshita, “Blood identification and discrimination between human and nonhuman blood using portable raman spectroscopy,” Int. J. Legal Med. 131(2), 319–322 (2017).
[Crossref]

Lawrence, S.

S. Lawrence, C. L. Giles, A. C. Tsoi, and A. D. Back, “Face recognition: A convolutional neural-network approach,” IEEE Trans. Neural Netw. Learn. Syst. 8(1), 98–113 (1997).
[Crossref]

Lednev, I. K.

K. C. Doty and I. K. Lednev, “Differentiating donor age groups based on raman spectroscopy of bloodstains for forensic purposes,” ACS Cent. Sci. 4(7), 862–867 (2018).
[Crossref]

K. C. Doty and I. K. Lednev, “Differentiation of human blood from animal blood using raman spectroscopy: A survey of forensically relevant species,” Forensic Sci. Int. 282, 204–210 (2018).
[Crossref]

A. Sikirzhytskaya, V. Sikirzhytski, and I. K. Lednev, “Determining gender by Raman spectroscopy of a bloodstain,” Anal. Chem. 89(3), 1486–1492 (2017).
[Crossref]

C. K. Muro, K. C. Doty, L. D. S. Fernandes, and I. K. Lednev, “Forensic body fluid identification and differentiation by raman spectroscopy,” Forensic Chem. 1, 31–38 (2016).
[Crossref]

E. Mistek, L. Halámková, K. C. Doty, C. K. Muro, and I. K. Lednev, “Race differentiation by raman spectroscopy of a bloodstain for forensic purposes,” Anal. Chem. 88(15), 7453–7456 (2016).
[Crossref]

E. Mistek and I. K. Lednev, “Identification of species’ blood by attenuated total reflection (ATR) Fourier transform infrared (FT-Ir) spectroscopy,” Anal. Bioanal. Chem. 407(24), 7435–7442 (2015).
[Crossref]

G. Mclaughlin, K. C. Doty, and I. K. Lednev, “Discrimination of human and animal blood traces via raman spectroscopy,” Forensic Sci. Int. 238, 91–95 (2014).
[Crossref]

G. Mclaughlin, K. C. Doty, I. K. Lednev, and A. Chem, “Raman spectroscopy of blood for species identification,” Anal. Chem. 86(23), 11628–11633 (2014).
[Crossref]

Lemler, P.

P. Lemler, W. R. Premasiri, A. Delmonaco, and L. D. Ziegler, “NIR Raman spectra of whole human blood: Effects of laser-induced and in vitro hemoglobin denaturation,” Anal. Bioanal. Chem. 406(1), 193–200 (2014).
[Crossref]

Lepot, L.

W. K. De, L. Lepot, F. Gason, and B. Gilbert, “In search of blood–detection of minute particles using spectroscopic methods,” Forensic Sci. Int. 180(1), 37–42 (2008).
[Crossref]

Lindley, N. C.

E. O. Espinoza, N. C. Lindley, K. M. Gordon, J. A. Ekhoff, and M. A. Kirms, “Electrospray ionization mass spectrometric analysis of blood for differentiation of species,” Anal. Biochem. 268(2), 252–261 (1999).
[Crossref]

Litjens, G.

T. Kooi, G. Litjens, G. B. Van, A. Gubern-Mérida, C. I. Sánchez, R. Mann, H. A. Den, and N. Karssemeijer, “Large scale deep learning for computer aided detection of mammographic lesions,” Med. Image Anal. 35, 303–312 (2017).
[Crossref]

A. A. A. Setio, F. Ciompi, G. Litjens, P. Gerke, C. Jacobs, S. J. V. Riel, M. M. W. Wille, M. Naqibullah, C. I. Sánchez, and B. V. Ginneken, “Pulmonary nodule detection in CT images: False positive reduction using multi-view convolutional networks,” IEEE T. Med. Imaging 35(5), 1160–1169 (2016).
[Crossref]

Liu, C. L.

C. L. Liu, W. H. Hsaio, and Y. C. Tu, “Time series classification with multivariate convolutional neural network,” IEEE Trans. Ind. Electron. 66(6), 4788–4797 (2019).
[Crossref]

Ma, S.

L. Zhang, Y. Wu, B. Zheng, L. Su, Y. Chen, S. Ma, Q. Hu, X. Zou, L. Yao, and Y. Yang, “Rapid histology of laryngeal squamous cell carcinoma with deep-learning based stimulated raman scattering microscopy,” Theranostics 9(9), 2541–2554 (2019).
[Crossref]

Maeno, Y.

H. Inouel, F. Takabe, O. Takenaka, M. Iwasa, and Y. Maeno, “Species identification of blood and bloodstains by high-performance liquid chromatography,” Int. J. Legal Med. 104(1), 9–12 (1990).
[Crossref]

Mann, R.

T. Kooi, G. Litjens, G. B. Van, A. Gubern-Mérida, C. I. Sánchez, R. Mann, H. A. Den, and N. Karssemeijer, “Large scale deep learning for computer aided detection of mammographic lesions,” Med. Image Anal. 35, 303–312 (2017).
[Crossref]

Mclaughlin, G.

G. Mclaughlin, K. C. Doty, I. K. Lednev, and A. Chem, “Raman spectroscopy of blood for species identification,” Anal. Chem. 86(23), 11628–11633 (2014).
[Crossref]

G. Mclaughlin, K. C. Doty, and I. K. Lednev, “Discrimination of human and animal blood traces via raman spectroscopy,” Forensic Sci. Int. 238, 91–95 (2014).
[Crossref]

Mcnaughton, D.

B. R. Wood, P. Caspers, G. J. Puppels, S. Pandiancherri, and D. Mcnaughton, “Resonance raman spectroscopy of red blood cells using near-infrared laser excitation,” Anal. Bioanal. Chem. 387(5), 1691–1703 (2007).
[Crossref]

Mistek, E.

E. Mistek, L. Halámková, K. C. Doty, C. K. Muro, and I. K. Lednev, “Race differentiation by raman spectroscopy of a bloodstain for forensic purposes,” Anal. Chem. 88(15), 7453–7456 (2016).
[Crossref]

E. Mistek and I. K. Lednev, “Identification of species’ blood by attenuated total reflection (ATR) Fourier transform infrared (FT-Ir) spectroscopy,” Anal. Bioanal. Chem. 407(24), 7435–7442 (2015).
[Crossref]

Muro, C. K.

E. Mistek, L. Halámková, K. C. Doty, C. K. Muro, and I. K. Lednev, “Race differentiation by raman spectroscopy of a bloodstain for forensic purposes,” Anal. Chem. 88(15), 7453–7456 (2016).
[Crossref]

C. K. Muro, K. C. Doty, L. D. S. Fernandes, and I. K. Lednev, “Forensic body fluid identification and differentiation by raman spectroscopy,” Forensic Chem. 1, 31–38 (2016).
[Crossref]

Naqibullah, M.

A. A. A. Setio, F. Ciompi, G. Litjens, P. Gerke, C. Jacobs, S. J. V. Riel, M. M. W. Wille, M. Naqibullah, C. I. Sánchez, and B. V. Ginneken, “Pulmonary nodule detection in CT images: False positive reduction using multi-view convolutional networks,” IEEE T. Med. Imaging 35(5), 1160–1169 (2016).
[Crossref]

Neumann, D.

S. C. Renner, D. Neumann, M. Burkart, U. Feit, P. Giere, A. Gröger, A. Paulsch, C. Paulsch, M. Sterz, and K. Vohland, “Import and export of biological samples from tropical countries–considerations and guidelines for research teams,” Org. Divers. Evol. 12(1), 81–98 (2012).
[Crossref]

Nishimoto, N.

J. Fujihara, Y. Fujita, T. Yamamoto, N. Nishimoto, K. Kimura-Kataoka, S. Kurata, Y. Takinami, T. Yasuda, and H. Takeshita, “Blood identification and discrimination between human and nonhuman blood using portable raman spectroscopy,” Int. J. Legal Med. 131(2), 319–322 (2017).
[Crossref]

Osindero, S.

G. E. Hinton, S. Osindero, and Y. W. Teh, “A fast learning algorithm for deep belief nets,” Neural Comput. 18(7), 1527–1554 (2006).
[Crossref]

Ozaki, Y.

H. Sato, H. Chiba, H. Tashiro, and Y. Ozaki, “Excitation wavelength-dependent changes in Raman spectra of whole blood and hemoglobin: Comparison of the spectra with 514.5-, 720-, and 1064-nm excitation,” J. Biomed. Opt. 6(3), 366–370 (2001).
[Crossref]

Pandiancherri, S.

B. R. Wood, P. Caspers, G. J. Puppels, S. Pandiancherri, and D. Mcnaughton, “Resonance raman spectroscopy of red blood cells using near-infrared laser excitation,” Anal. Bioanal. Chem. 387(5), 1691–1703 (2007).
[Crossref]

Paulsch, A.

S. C. Renner, D. Neumann, M. Burkart, U. Feit, P. Giere, A. Gröger, A. Paulsch, C. Paulsch, M. Sterz, and K. Vohland, “Import and export of biological samples from tropical countries–considerations and guidelines for research teams,” Org. Divers. Evol. 12(1), 81–98 (2012).
[Crossref]

Paulsch, C.

S. C. Renner, D. Neumann, M. Burkart, U. Feit, P. Giere, A. Gröger, A. Paulsch, C. Paulsch, M. Sterz, and K. Vohland, “Import and export of biological samples from tropical countries–considerations and guidelines for research teams,” Org. Divers. Evol. 12(1), 81–98 (2012).
[Crossref]

Pezoulas, V. C.

K. Tsiouris, V. C. Pezoulas, M. Zervakis, S. Konitsiotis, D. D. Koutsouris, and D. I. Fotiadis, “A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals,” Comput. Biol. Med. 99, 24–37 (2018).
[Crossref]

Premasiri, W. R.

P. Lemler, W. R. Premasiri, A. Delmonaco, and L. D. Ziegler, “NIR Raman spectra of whole human blood: Effects of laser-induced and in vitro hemoglobin denaturation,” Anal. Bioanal. Chem. 406(1), 193–200 (2014).
[Crossref]

Prinz, M.

H. Yang, B. Zhou, M. Prinz, D. Siegel, and H. Deng, “Body fluid identification by mass spectrometry,” Int. J. Legal Med. 127(6), 1065–1077 (2013).
[Crossref]

Puppels, G. J.

B. R. Wood, P. Caspers, G. J. Puppels, S. Pandiancherri, and D. Mcnaughton, “Resonance raman spectroscopy of red blood cells using near-infrared laser excitation,” Anal. Bioanal. Chem. 387(5), 1691–1703 (2007).
[Crossref]

Qin, J.

Q. Dou, H. Chen, L. Yu, J. Qin, and P. A. Heng, “Multi-level contextual 3d cnns for false positive reduction in pulmonary nodule detection,” IEEE Trans. Biomed. Eng. 64(7), 1558–1567 (2017).
[Crossref]

Reinke, A. K.

E. Sauer, A. K. Reinke, and C. Courts, “Differentiation of five body fluids from forensic samples by expression analysis of four micrornas using quantitative pcr,” Forensic Sci. Int.: Genet. 22, 89–99 (2016).
[Crossref]

Renner, S. C.

S. C. Renner, D. Neumann, M. Burkart, U. Feit, P. Giere, A. Gröger, A. Paulsch, C. Paulsch, M. Sterz, and K. Vohland, “Import and export of biological samples from tropical countries–considerations and guidelines for research teams,” Org. Divers. Evol. 12(1), 81–98 (2012).
[Crossref]

Riel, S. J. V.

A. A. A. Setio, F. Ciompi, G. Litjens, P. Gerke, C. Jacobs, S. J. V. Riel, M. M. W. Wille, M. Naqibullah, C. I. Sánchez, and B. V. Ginneken, “Pulmonary nodule detection in CT images: False positive reduction using multi-view convolutional networks,” IEEE T. Med. Imaging 35(5), 1160–1169 (2016).
[Crossref]

Sánchez, C. I.

T. Kooi, G. Litjens, G. B. Van, A. Gubern-Mérida, C. I. Sánchez, R. Mann, H. A. Den, and N. Karssemeijer, “Large scale deep learning for computer aided detection of mammographic lesions,” Med. Image Anal. 35, 303–312 (2017).
[Crossref]

A. A. A. Setio, F. Ciompi, G. Litjens, P. Gerke, C. Jacobs, S. J. V. Riel, M. M. W. Wille, M. Naqibullah, C. I. Sánchez, and B. V. Ginneken, “Pulmonary nodule detection in CT images: False positive reduction using multi-view convolutional networks,” IEEE T. Med. Imaging 35(5), 1160–1169 (2016).
[Crossref]

Sato, H.

H. Sato, H. Chiba, H. Tashiro, and Y. Ozaki, “Excitation wavelength-dependent changes in Raman spectra of whole blood and hemoglobin: Comparison of the spectra with 514.5-, 720-, and 1064-nm excitation,” J. Biomed. Opt. 6(3), 366–370 (2001).
[Crossref]

Sauer, E.

E. Sauer, A. K. Reinke, and C. Courts, “Differentiation of five body fluids from forensic samples by expression analysis of four micrornas using quantitative pcr,” Forensic Sci. Int.: Genet. 22, 89–99 (2016).
[Crossref]

Setio, A. A. A.

A. A. A. Setio, F. Ciompi, G. Litjens, P. Gerke, C. Jacobs, S. J. V. Riel, M. M. W. Wille, M. Naqibullah, C. I. Sánchez, and B. V. Ginneken, “Pulmonary nodule detection in CT images: False positive reduction using multi-view convolutional networks,” IEEE T. Med. Imaging 35(5), 1160–1169 (2016).
[Crossref]

Shalhon, M.

A. J. Bekker, M. Shalhon, H. Greenspan, and J. Goldberger, “Multi-view probabilistic classification of breast microcalcifications,” IEEE T. Med. Imaging 35(2), 645–653 (2016).
[Crossref]

Siegel, D.

H. Yang, B. Zhou, M. Prinz, D. Siegel, and H. Deng, “Body fluid identification by mass spectrometry,” Int. J. Legal Med. 127(6), 1065–1077 (2013).
[Crossref]

Sikirzhytskaya, A.

A. Sikirzhytskaya, V. Sikirzhytski, and I. K. Lednev, “Determining gender by Raman spectroscopy of a bloodstain,” Anal. Chem. 89(3), 1486–1492 (2017).
[Crossref]

Sikirzhytski, V.

A. Sikirzhytskaya, V. Sikirzhytski, and I. K. Lednev, “Determining gender by Raman spectroscopy of a bloodstain,” Anal. Chem. 89(3), 1486–1492 (2017).
[Crossref]

Sterz, M.

S. C. Renner, D. Neumann, M. Burkart, U. Feit, P. Giere, A. Gröger, A. Paulsch, C. Paulsch, M. Sterz, and K. Vohland, “Import and export of biological samples from tropical countries–considerations and guidelines for research teams,” Org. Divers. Evol. 12(1), 81–98 (2012).
[Crossref]

Su, L.

L. Zhang, Y. Wu, B. Zheng, L. Su, Y. Chen, S. Ma, Q. Hu, X. Zou, L. Yao, and Y. Yang, “Rapid histology of laryngeal squamous cell carcinoma with deep-learning based stimulated raman scattering microscopy,” Theranostics 9(9), 2541–2554 (2019).
[Crossref]

Takabe, F.

H. Inouel, F. Takabe, O. Takenaka, M. Iwasa, and Y. Maeno, “Species identification of blood and bloodstains by high-performance liquid chromatography,” Int. J. Legal Med. 104(1), 9–12 (1990).
[Crossref]

Takenaka, O.

H. Inouel, F. Takabe, O. Takenaka, M. Iwasa, and Y. Maeno, “Species identification of blood and bloodstains by high-performance liquid chromatography,” Int. J. Legal Med. 104(1), 9–12 (1990).
[Crossref]

Takeshita, H.

J. Fujihara, Y. Fujita, T. Yamamoto, N. Nishimoto, K. Kimura-Kataoka, S. Kurata, Y. Takinami, T. Yasuda, and H. Takeshita, “Blood identification and discrimination between human and nonhuman blood using portable raman spectroscopy,” Int. J. Legal Med. 131(2), 319–322 (2017).
[Crossref]

Takinami, Y.

J. Fujihara, Y. Fujita, T. Yamamoto, N. Nishimoto, K. Kimura-Kataoka, S. Kurata, Y. Takinami, T. Yasuda, and H. Takeshita, “Blood identification and discrimination between human and nonhuman blood using portable raman spectroscopy,” Int. J. Legal Med. 131(2), 319–322 (2017).
[Crossref]

Tashiro, H.

H. Sato, H. Chiba, H. Tashiro, and Y. Ozaki, “Excitation wavelength-dependent changes in Raman spectra of whole blood and hemoglobin: Comparison of the spectra with 514.5-, 720-, and 1064-nm excitation,” J. Biomed. Opt. 6(3), 366–370 (2001).
[Crossref]

Teh, Y. W.

G. E. Hinton, S. Osindero, and Y. W. Teh, “A fast learning algorithm for deep belief nets,” Neural Comput. 18(7), 1527–1554 (2006).
[Crossref]

Tian, Y.

H. Bian, P. Wang, N. Wang, Y. Tian, P. Bai, H. Jiang, and J. Gao, “Dual-model analysis for improving the discrimination performance of human and nonhuman blood based on raman spectroscopy,” Biomed. Opt. Express 9(8), 3512–3522 (2018).
[Crossref]

P. Bai, J. Wang, H. Yin, Y. Tian, W. Yao, and G. Jing, “Discrimination of human and nonhuman blood by raman spectroscopy and partial least squares discriminant analysis,” Anal. Lett. 50(2), 379–388 (2017).
[Crossref]

Tsiouris, K.

K. Tsiouris, V. C. Pezoulas, M. Zervakis, S. Konitsiotis, D. D. Koutsouris, and D. I. Fotiadis, “A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals,” Comput. Biol. Med. 99, 24–37 (2018).
[Crossref]

Tsoi, A. C.

S. Lawrence, C. L. Giles, A. C. Tsoi, and A. D. Back, “Face recognition: A convolutional neural-network approach,” IEEE Trans. Neural Netw. Learn. Syst. 8(1), 98–113 (1997).
[Crossref]

Tu, Y. C.

C. L. Liu, W. H. Hsaio, and Y. C. Tu, “Time series classification with multivariate convolutional neural network,” IEEE Trans. Ind. Electron. 66(6), 4788–4797 (2019).
[Crossref]

Turner, R. F.

Van, G. B.

T. Kooi, G. Litjens, G. B. Van, A. Gubern-Mérida, C. I. Sánchez, R. Mann, H. A. Den, and N. Karssemeijer, “Large scale deep learning for computer aided detection of mammographic lesions,” Med. Image Anal. 35, 303–312 (2017).
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Figures (9)

Fig. 1.
Fig. 1. Diagram of the experimental device.
Fig. 2.
Fig. 2. The effects of baseline correction and normalization. (a) Raman spectrum before baseline correction (b) Raman spectrum after baseline correction, (c) Raman spectrum after baseline correction and normalization
Fig. 3.
Fig. 3. Training and test flow charts
Fig. 4.
Fig. 4. Architecture diagram of blood recognition based on one-dimensional convolutional neural network model.
Fig. 5.
Fig. 5. Raman spectra of cryopreserved liquid blood at different times.
Fig. 6.
Fig. 6. Average Raman spectrum comparison of 20 kinds of blood species. The locations of the main characteristic peaks are shown. According to these locations, the corresponding vibration modes can be known and the blood components can be judged, thus realizing biochemical analysis.
Fig. 7.
Fig. 7. Record of Adjusting Parameters
Fig. 8.
Fig. 8. Multiple classification of confusion matrix and normalized confusion matrix. (a) The abscissa corresponds to the predicted blood species and the ordinate corresponds to the actual blood species. The number of samples appearing on the diagonal line, that is, the predicted values were consistent with the actual values, which can be judged as the number of samples correctly identified, and the data appearing outside the diagonal line, that is the number of samples confused by recognition. The legend on the right indicates that darker the color is, the more samples occupied. (b) The coordinate interpretation as figure (a). Data were normalized. The proportions that appear on the diagonal line were predicted correctly, and those outside the diagonal line were confused proportions.
Fig. 9.
Fig. 9. ROC curve and AUC of multiple blood classification.

Tables (6)

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Table 1. Sample size of all species and partitions of the all spectral data

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Table 2. Band position, assignment and local coordinate for Raman spectra of whole blood

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Table 3. Default and Optimal Parameters for CNN models of Blood Recognition

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Table 4. Kappa coefficient consistency scale

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Table 5. 3×3 contingency tables

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Table 6. Performance measurement of the classification model

Equations (5)

Equations on this page are rendered with MathJax. Learn more.

S o f t max ( x i ) = e x j j = 1 j e x j ( i = 1 , , j )
M a c r o P = 1 n i = 1 n P i
M a c r o R = 1 n i = 1 n R i
M a c r o F β = ( 1 + β 2 ) × ( M a c r o P ) × ( M a c r o R ) ( β 2 × M a c r o P ) + ( M a c r o R )
κ = P o P e 1 P e , ( P o = a i i N , P e = A i B i N 2 ) ( i = 1 , 2 , 3 )