Abstract

We present a spatio-temporal analysis of cell membrane fluctuations to distinguish healthy patients from patients with sickle cell disease. A video hologram containing either healthy red blood cells (h-RBCs) or sickle cell disease red blood cells (SCD-RBCs) was recorded using a low-cost, compact, 3D printed shearing interferometer. Reconstructions were created for each hologram frame (time steps), forming a spatio-temporal data cube. Features were extracted by computing the standard deviations and the mean of the height fluctuations over time and for every location on the cell membrane, resulting in two-dimensional standard deviation and mean maps, followed by taking the standard deviations of these maps. The optical flow algorithm was used to estimate the apparent motion fields between subsequent frames (reconstructions). The standard deviation of the magnitude of the optical flow vectors across all frames was then computed. In addition, seven morphological cell (spatial) features based on optical path length were extracted from the cells to further improve the classification accuracy. A random forest classifier was trained to perform cell identification to distinguish between SCD-RBCs and h-RBCs. To the best of our knowledge, this is the first report of machine learning assisted cell identification and diagnosis of sickle cell disease based on cell membrane fluctuations and morphology using both spatio-temporal and spatial analysis.

© 2018 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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References

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2017 (4)

A. Anand, I. K. Moon, and B. Javidi, “Automated Disease Identification with 3-D Optical Imaging: A Medical Diagnostic Tool,” Proc. IEEE 105(5), 924–946 (2017).
[Crossref]

S. Rawat, S. Komatsu, A. Markman, A. Anand, and B. Javidi, “Compact and field-portable 3D printed shearing digital holographic microscope for automated cell identification,” Appl. Opt. 56(9), D127–D133 (2017).
[Crossref] [PubMed]

O. Matoba, X. Quan, P. Xia, Y. Awatsuji, and T. Nomura, “Multimodal Imaging Based on Digital Holography,” Proc. IEEE 105(5), 906–923 (2017).
[Crossref]

Y. Jo, S. Park, J. Jung, J. Yoon, H. Joo, M. H. Kim, S. J. Kang, M. C. Choi, S. Y. Lee, and Y. Park, “Holographic deep learning for rapid optical screening of anthrax spores,” Sci. Adv. 3(8), e1700606 (2017).
[Crossref] [PubMed]

2016 (1)

2015 (1)

P. Memmolo, L. Miccio, M. Paturzo, G. Caprio, G. Coppola, P. Netti, and P. Ferraro, “Recent advances in holographic 3D particle tracking,” Adv. Opt. Photonics 7(4), 713–755 (2015).
[Crossref]

2014 (1)

A. Anand, A. Faridian, V. Chhaniwal, S. Mahajan, V. Trivedi, S. Dubey, G. Pedrini, W. Osten, and B. Javidi, “Single beam Fourier transform digital holographic quantitative phase microscopy,” Appl. Phys. Lett. 104(10), 103705 (2014).
[Crossref]

2013 (2)

I. Moon, A. Anand, M. Cruz, and B. Javidi, “Identification of Malaria Infected Red Blood Cells via Digital Shearing Interferometry and Statistical Inference,” IEEE Photonics J. 5(5), 6900207 (2013).
[Crossref]

Y. Cotte, F. Toy, P. Jourdain, N. Pavillon, D. Boss, P. Magistretti, P. Marquet, and C. Depeursinge, “Marker-free phase nanoscopy,” Nat. Photonics 7(2), 113–117 (2013).
[Crossref]

2012 (4)

2011 (1)

N. T. Shaked, L. L. Satterwhite, M. J. Telen, G. A. Truskey, and A. Wax, “Quantitative microscopy and nanoscopy of sickle red blood cells performed by wide field digital interferometry,” J. Biomed. Opt. 16(3), 030506 (2011).
[Crossref] [PubMed]

2006 (1)

2005 (2)

2001 (1)

L. Breiman, “Random Forests,” Mach. Learn. 45(1), 5–32 (2001).
[Crossref]

2000 (1)

1998 (1)

1997 (1)

R. P. Shukla and D. Malacara, “Some applications of the Murty interferometer: a review,” Opt. Lasers Eng. 26(1), 1–42 (1997).
[Crossref]

1988 (1)

R. M. Goldstein, H. A. Zebker, and C. L. Werner, “Satellite radar interferometry: two-dimensional phase unwrapping,” Radio Sci. 23(4), 713–720 (1988).
[Crossref]

1981 (1)

B. Horn and B. Schunck, “Determining optical flow,” Artif. Intell. 17(1–3), 185–203 (1981).
[Crossref]

Anand, A.

A. Anand, I. K. Moon, and B. Javidi, “Automated Disease Identification with 3-D Optical Imaging: A Medical Diagnostic Tool,” Proc. IEEE 105(5), 924–946 (2017).
[Crossref]

S. Rawat, S. Komatsu, A. Markman, A. Anand, and B. Javidi, “Compact and field-portable 3D printed shearing digital holographic microscope for automated cell identification,” Appl. Opt. 56(9), D127–D133 (2017).
[Crossref] [PubMed]

A. Anand, A. Faridian, V. Chhaniwal, S. Mahajan, V. Trivedi, S. Dubey, G. Pedrini, W. Osten, and B. Javidi, “Single beam Fourier transform digital holographic quantitative phase microscopy,” Appl. Phys. Lett. 104(10), 103705 (2014).
[Crossref]

I. Moon, A. Anand, M. Cruz, and B. Javidi, “Identification of Malaria Infected Red Blood Cells via Digital Shearing Interferometry and Statistical Inference,” IEEE Photonics J. 5(5), 6900207 (2013).
[Crossref]

A. Anand, V. K. Chhaniwal, N. R. Patel, and B. Javidi, “Automatic identification of malaria-infected RBC with digital holographic microscopy using correlation algorithms,” IEEE Photonics J. 4(5), 1456–1464 (2012).
[Crossref]

V. Chhaniwal, A. S. G. Singh, R. A. Leitgeb, B. Javidi, and A. Anand, “Quantitative phase-contrast imaging with compact digital holographic microscope employing Lloyd’s mirror,” Opt. Lett. 37(24), 5127–5129 (2012).
[Crossref] [PubMed]

A. S. Singh, A. Anand, R. A. Leitgeb, and B. Javidi, “Lateral shearing digital holographic imaging of small biological specimens,” Opt. Express 20(21), 23617–23622 (2012).
[Crossref] [PubMed]

Awatsuji, Y.

O. Matoba, X. Quan, P. Xia, Y. Awatsuji, and T. Nomura, “Multimodal Imaging Based on Digital Holography,” Proc. IEEE 105(5), 906–923 (2017).
[Crossref]

Boss, D.

Y. Cotte, F. Toy, P. Jourdain, N. Pavillon, D. Boss, P. Magistretti, P. Marquet, and C. Depeursinge, “Marker-free phase nanoscopy,” Nat. Photonics 7(2), 113–117 (2013).
[Crossref]

Breiman, L.

L. Breiman, “Random Forests,” Mach. Learn. 45(1), 5–32 (2001).
[Crossref]

Caprio, G.

P. Memmolo, L. Miccio, M. Paturzo, G. Caprio, G. Coppola, P. Netti, and P. Ferraro, “Recent advances in holographic 3D particle tracking,” Adv. Opt. Photonics 7(4), 713–755 (2015).
[Crossref]

Carapezza, E.

Chhaniwal, V.

A. Anand, A. Faridian, V. Chhaniwal, S. Mahajan, V. Trivedi, S. Dubey, G. Pedrini, W. Osten, and B. Javidi, “Single beam Fourier transform digital holographic quantitative phase microscopy,” Appl. Phys. Lett. 104(10), 103705 (2014).
[Crossref]

V. Chhaniwal, A. S. G. Singh, R. A. Leitgeb, B. Javidi, and A. Anand, “Quantitative phase-contrast imaging with compact digital holographic microscope employing Lloyd’s mirror,” Opt. Lett. 37(24), 5127–5129 (2012).
[Crossref] [PubMed]

Chhaniwal, V. K.

A. Anand, V. K. Chhaniwal, N. R. Patel, and B. Javidi, “Automatic identification of malaria-infected RBC with digital holographic microscopy using correlation algorithms,” IEEE Photonics J. 4(5), 1456–1464 (2012).
[Crossref]

Choi, M. C.

Y. Jo, S. Park, J. Jung, J. Yoon, H. Joo, M. H. Kim, S. J. Kang, M. C. Choi, S. Y. Lee, and Y. Park, “Holographic deep learning for rapid optical screening of anthrax spores,” Sci. Adv. 3(8), e1700606 (2017).
[Crossref] [PubMed]

Coppola, G.

P. Memmolo, L. Miccio, M. Paturzo, G. Caprio, G. Coppola, P. Netti, and P. Ferraro, “Recent advances in holographic 3D particle tracking,” Adv. Opt. Photonics 7(4), 713–755 (2015).
[Crossref]

Cotte, Y.

Y. Cotte, F. Toy, P. Jourdain, N. Pavillon, D. Boss, P. Magistretti, P. Marquet, and C. Depeursinge, “Marker-free phase nanoscopy,” Nat. Photonics 7(2), 113–117 (2013).
[Crossref]

Cruz, M.

I. Moon, A. Anand, M. Cruz, and B. Javidi, “Identification of Malaria Infected Red Blood Cells via Digital Shearing Interferometry and Statistical Inference,” IEEE Photonics J. 5(5), 6900207 (2013).
[Crossref]

Depeursinge, C.

Y. Cotte, F. Toy, P. Jourdain, N. Pavillon, D. Boss, P. Magistretti, P. Marquet, and C. Depeursinge, “Marker-free phase nanoscopy,” Nat. Photonics 7(2), 113–117 (2013).
[Crossref]

Dubey, S.

A. Anand, A. Faridian, V. Chhaniwal, S. Mahajan, V. Trivedi, S. Dubey, G. Pedrini, W. Osten, and B. Javidi, “Single beam Fourier transform digital holographic quantitative phase microscopy,” Appl. Phys. Lett. 104(10), 103705 (2014).
[Crossref]

Dubois, F.

Faridian, A.

A. Anand, A. Faridian, V. Chhaniwal, S. Mahajan, V. Trivedi, S. Dubey, G. Pedrini, W. Osten, and B. Javidi, “Single beam Fourier transform digital holographic quantitative phase microscopy,” Appl. Phys. Lett. 104(10), 103705 (2014).
[Crossref]

Ferraro, P.

P. Memmolo, L. Miccio, M. Paturzo, G. Caprio, G. Coppola, P. Netti, and P. Ferraro, “Recent advances in holographic 3D particle tracking,” Adv. Opt. Photonics 7(4), 713–755 (2015).
[Crossref]

Garcia-Sucerquia, J.

Goldstein, R. M.

R. M. Goldstein, H. A. Zebker, and C. L. Werner, “Satellite radar interferometry: two-dimensional phase unwrapping,” Radio Sci. 23(4), 713–720 (1988).
[Crossref]

Hammer, M.

Horn, B.

B. Horn and B. Schunck, “Determining optical flow,” Artif. Intell. 17(1–3), 185–203 (1981).
[Crossref]

Javidi, B.

A. Anand, I. K. Moon, and B. Javidi, “Automated Disease Identification with 3-D Optical Imaging: A Medical Diagnostic Tool,” Proc. IEEE 105(5), 924–946 (2017).
[Crossref]

S. Rawat, S. Komatsu, A. Markman, A. Anand, and B. Javidi, “Compact and field-portable 3D printed shearing digital holographic microscope for automated cell identification,” Appl. Opt. 56(9), D127–D133 (2017).
[Crossref] [PubMed]

F. Yi, I. Moon, and B. Javidi, “Cell morphology-based classification of red blood cells using holographic imaging informatics,” Biomed. Opt. Express 7(6), 2385–2399 (2016).
[Crossref] [PubMed]

A. Anand, A. Faridian, V. Chhaniwal, S. Mahajan, V. Trivedi, S. Dubey, G. Pedrini, W. Osten, and B. Javidi, “Single beam Fourier transform digital holographic quantitative phase microscopy,” Appl. Phys. Lett. 104(10), 103705 (2014).
[Crossref]

I. Moon, A. Anand, M. Cruz, and B. Javidi, “Identification of Malaria Infected Red Blood Cells via Digital Shearing Interferometry and Statistical Inference,” IEEE Photonics J. 5(5), 6900207 (2013).
[Crossref]

A. Anand, V. K. Chhaniwal, N. R. Patel, and B. Javidi, “Automatic identification of malaria-infected RBC with digital holographic microscopy using correlation algorithms,” IEEE Photonics J. 4(5), 1456–1464 (2012).
[Crossref]

V. Chhaniwal, A. S. G. Singh, R. A. Leitgeb, B. Javidi, and A. Anand, “Quantitative phase-contrast imaging with compact digital holographic microscope employing Lloyd’s mirror,” Opt. Lett. 37(24), 5127–5129 (2012).
[Crossref] [PubMed]

A. S. Singh, A. Anand, R. A. Leitgeb, and B. Javidi, “Lateral shearing digital holographic imaging of small biological specimens,” Opt. Express 20(21), 23617–23622 (2012).
[Crossref] [PubMed]

I. Moon and B. Javidi, “Shape tolerant three-dimensional recognition of biological microorganisms using digital holography,” Opt. Express 13(23), 9612–9622 (2005).
[Crossref] [PubMed]

B. Javidi, I. Moon, S. Yeom, and E. Carapezza, “Three-dimensional imaging and recognition of microorganism using single-exposure on-line (SEOL) digital holography,” Opt. Express 13(12), 4492–4506 (2005).
[Crossref] [PubMed]

B. Javidi and E. Tajahuerce, “Three-dimensional object recognition by use of digital holography,” Opt. Lett. 25(9), 610–612 (2000).
[Crossref] [PubMed]

Jericho, M. H.

Jericho, S. K.

Jo, Y.

Y. Jo, S. Park, J. Jung, J. Yoon, H. Joo, M. H. Kim, S. J. Kang, M. C. Choi, S. Y. Lee, and Y. Park, “Holographic deep learning for rapid optical screening of anthrax spores,” Sci. Adv. 3(8), e1700606 (2017).
[Crossref] [PubMed]

Joo, H.

Y. Jo, S. Park, J. Jung, J. Yoon, H. Joo, M. H. Kim, S. J. Kang, M. C. Choi, S. Y. Lee, and Y. Park, “Holographic deep learning for rapid optical screening of anthrax spores,” Sci. Adv. 3(8), e1700606 (2017).
[Crossref] [PubMed]

Jourdain, P.

Y. Cotte, F. Toy, P. Jourdain, N. Pavillon, D. Boss, P. Magistretti, P. Marquet, and C. Depeursinge, “Marker-free phase nanoscopy,” Nat. Photonics 7(2), 113–117 (2013).
[Crossref]

Jung, J.

Y. Jo, S. Park, J. Jung, J. Yoon, H. Joo, M. H. Kim, S. J. Kang, M. C. Choi, S. Y. Lee, and Y. Park, “Holographic deep learning for rapid optical screening of anthrax spores,” Sci. Adv. 3(8), e1700606 (2017).
[Crossref] [PubMed]

Kang, S. J.

Y. Jo, S. Park, J. Jung, J. Yoon, H. Joo, M. H. Kim, S. J. Kang, M. C. Choi, S. Y. Lee, and Y. Park, “Holographic deep learning for rapid optical screening of anthrax spores,” Sci. Adv. 3(8), e1700606 (2017).
[Crossref] [PubMed]

Kim, M. H.

Y. Jo, S. Park, J. Jung, J. Yoon, H. Joo, M. H. Kim, S. J. Kang, M. C. Choi, S. Y. Lee, and Y. Park, “Holographic deep learning for rapid optical screening of anthrax spores,” Sci. Adv. 3(8), e1700606 (2017).
[Crossref] [PubMed]

Klages, P.

Kolb, A.

Komatsu, S.

Kreuzer, H. J.

Lee, S. Y.

Y. Jo, S. Park, J. Jung, J. Yoon, H. Joo, M. H. Kim, S. J. Kang, M. C. Choi, S. Y. Lee, and Y. Park, “Holographic deep learning for rapid optical screening of anthrax spores,” Sci. Adv. 3(8), e1700606 (2017).
[Crossref] [PubMed]

Leitgeb, R. A.

Magistretti, P.

Y. Cotte, F. Toy, P. Jourdain, N. Pavillon, D. Boss, P. Magistretti, P. Marquet, and C. Depeursinge, “Marker-free phase nanoscopy,” Nat. Photonics 7(2), 113–117 (2013).
[Crossref]

Mahajan, S.

A. Anand, A. Faridian, V. Chhaniwal, S. Mahajan, V. Trivedi, S. Dubey, G. Pedrini, W. Osten, and B. Javidi, “Single beam Fourier transform digital holographic quantitative phase microscopy,” Appl. Phys. Lett. 104(10), 103705 (2014).
[Crossref]

Malacara, D.

R. P. Shukla and D. Malacara, “Some applications of the Murty interferometer: a review,” Opt. Lasers Eng. 26(1), 1–42 (1997).
[Crossref]

Markman, A.

Marquet, P.

Y. Cotte, F. Toy, P. Jourdain, N. Pavillon, D. Boss, P. Magistretti, P. Marquet, and C. Depeursinge, “Marker-free phase nanoscopy,” Nat. Photonics 7(2), 113–117 (2013).
[Crossref]

Matoba, O.

O. Matoba, X. Quan, P. Xia, Y. Awatsuji, and T. Nomura, “Multimodal Imaging Based on Digital Holography,” Proc. IEEE 105(5), 906–923 (2017).
[Crossref]

Memmolo, P.

P. Memmolo, L. Miccio, M. Paturzo, G. Caprio, G. Coppola, P. Netti, and P. Ferraro, “Recent advances in holographic 3D particle tracking,” Adv. Opt. Photonics 7(4), 713–755 (2015).
[Crossref]

Miccio, L.

P. Memmolo, L. Miccio, M. Paturzo, G. Caprio, G. Coppola, P. Netti, and P. Ferraro, “Recent advances in holographic 3D particle tracking,” Adv. Opt. Photonics 7(4), 713–755 (2015).
[Crossref]

Michel, B.

Moon, I.

Moon, I. K.

A. Anand, I. K. Moon, and B. Javidi, “Automated Disease Identification with 3-D Optical Imaging: A Medical Diagnostic Tool,” Proc. IEEE 105(5), 924–946 (2017).
[Crossref]

Netti, P.

P. Memmolo, L. Miccio, M. Paturzo, G. Caprio, G. Coppola, P. Netti, and P. Ferraro, “Recent advances in holographic 3D particle tracking,” Adv. Opt. Photonics 7(4), 713–755 (2015).
[Crossref]

Nomura, T.

O. Matoba, X. Quan, P. Xia, Y. Awatsuji, and T. Nomura, “Multimodal Imaging Based on Digital Holography,” Proc. IEEE 105(5), 906–923 (2017).
[Crossref]

Osten, W.

A. Anand, A. Faridian, V. Chhaniwal, S. Mahajan, V. Trivedi, S. Dubey, G. Pedrini, W. Osten, and B. Javidi, “Single beam Fourier transform digital holographic quantitative phase microscopy,” Appl. Phys. Lett. 104(10), 103705 (2014).
[Crossref]

Park, S.

Y. Jo, S. Park, J. Jung, J. Yoon, H. Joo, M. H. Kim, S. J. Kang, M. C. Choi, S. Y. Lee, and Y. Park, “Holographic deep learning for rapid optical screening of anthrax spores,” Sci. Adv. 3(8), e1700606 (2017).
[Crossref] [PubMed]

Park, Y.

Y. Jo, S. Park, J. Jung, J. Yoon, H. Joo, M. H. Kim, S. J. Kang, M. C. Choi, S. Y. Lee, and Y. Park, “Holographic deep learning for rapid optical screening of anthrax spores,” Sci. Adv. 3(8), e1700606 (2017).
[Crossref] [PubMed]

Patel, N. R.

A. Anand, V. K. Chhaniwal, N. R. Patel, and B. Javidi, “Automatic identification of malaria-infected RBC with digital holographic microscopy using correlation algorithms,” IEEE Photonics J. 4(5), 1456–1464 (2012).
[Crossref]

Paturzo, M.

P. Memmolo, L. Miccio, M. Paturzo, G. Caprio, G. Coppola, P. Netti, and P. Ferraro, “Recent advances in holographic 3D particle tracking,” Adv. Opt. Photonics 7(4), 713–755 (2015).
[Crossref]

Pavillon, N.

Y. Cotte, F. Toy, P. Jourdain, N. Pavillon, D. Boss, P. Magistretti, P. Marquet, and C. Depeursinge, “Marker-free phase nanoscopy,” Nat. Photonics 7(2), 113–117 (2013).
[Crossref]

Pedrini, G.

A. Anand, A. Faridian, V. Chhaniwal, S. Mahajan, V. Trivedi, S. Dubey, G. Pedrini, W. Osten, and B. Javidi, “Single beam Fourier transform digital holographic quantitative phase microscopy,” Appl. Phys. Lett. 104(10), 103705 (2014).
[Crossref]

Quan, X.

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Supplementary Material (1)

NameDescription
» Visualization 1       Red blood cell fluctuations

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Figures (10)

Figure 1
Figure 1 Experimental setup for the (a) proposed common-path biosensor based on shearing DHM. (b) Compact 3D printed prototype of the DH microscope with the dimensions of 90 mm × 85 mm × 200 mm.
Fig. 2
Fig. 2 Thickness profile for blood smears from (a) a healthy volunteer and (b) a patient with SCD.
Fig. 3
Fig. 3 Pseudo-color 3D reconstructions for (a) a healthy RBC and (b) a round sickle (left) and a crescent shaped sickle cell disease RBC (right).
Fig. 4
Fig. 4 Experimental results for the temporal stability of the compact 3D printed prototype [see Fig. 1(b)] in a clinical setting.
Fig. 5
Fig. 5 (a) 3D pseudo color reconstruction video frame for an h-RBC depicting the cell thickness. (b) Top view of the same h-RBC. See Visualization 1 for the full video.
Fig. 6
Fig. 6 Cell membrane fluctuations for three different spatial locations (A, B, and C) on an h-RBC’s membrane [see Fig. 5(b)]. σ = standard deviation.
Fig. 7
Fig. 7 (a) Stack of 3D optical path length (OPL) reconstructions for a h-RBC at different time intervals and (b) a data cube of 3D cell reconstructions recorded over time t. Red box in temporal cube represents a single pixel stack, each element of this stack contains membrane fluctuation information at any time instance.
Fig. 8
Fig. 8 (a) The 2D mean pixel map, and (b) 2D standard deviation (STD) pixel map, computed by taking the mean and standard deviation, respectively, of the spatio-temporal cube consisting of 3D reconstructed holograms over time t along the t dimension. (c) Optical flow vectors (shown by a quiver plot) for a healthy (segmented) RBC between two successive 3D reconstructed OPL frames.
Fig. 9
Fig. 9 Density plots of three spatio-temporal and seven morphological features extracted from the cell data. OF = optical flow, STD_MEAN = standard deviation of the 2D mean map, STD_STD = standard deviation of the standard deviation map, M-OPL = mean of optical path length values, COV = coefficient of variation, OPT_VOL (OV) = optical volume based on OPL, PROJ_AREA (PA) = projected cell area based on OPL, PA/OV = ratio of PA over OV, SKEWNESS = skewness based on OPL, KURTOSIS = kurtosis based on OPL. The spatio-temporal feature labels are bounded by a red box, and OPL based morphological features labels by a green box.
Fig. 10
Fig. 10 (a) Predictor importance estimates for the 10 features [see Fig. 9)] Features are numbered 1-10 and represent optical flow, standard deviation of the 2D mean map, standard deviation of the standard deviation map, mean optical path length, coefficient of variation, optical volume, projected area, projected area to optical volume ratio, skewness, and kurtosis, respectively.

Tables (5)

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Table 1 Demographic and Clinical Comparison of Healthy Controls vs. SCD Subjects

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Table 2 Confusion matrix for classification of healthy RBCs and SCD-RBC.

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Table 3 Classification output for disease detection of patients using only spatio-temporal-based features.

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Table 4 Classification output for disease detection of patients using only morphology -based features

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Table 5 Classification output for disease detection of patients using both morphological and spatio-temporal-based features

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