Reference | Network | Species/classes | Species size | Parameters included | Type of parameter | Instrument | Accuracy | |
---|---|---|---|---|---|---|---|---|
Flow cytometric data (scatter and fluorescence values) | Frankel et al. [19] | ANN (Kohonen network/Back-propagation neural networks) | 5 | Picoplankton, large phytoplankton | 5 | FSC 488 nm Ex./540–630 nm 514 nm Ex./540–630 nm 488 nm Ex./660–700 nm 514 nm Ex./660–700 nm | EPICS V | 92–100% |
Balfoort et al. [1] | Multilayer feedforward network (NWorks, ANNET, 8) | 8 | 3–3500 µm | 6 | FSC, SSC, TOF 488 nm Ex./515–600 nm Em 488 nm Ex./650–750 nm Em 633 nm Ex./650–750 nm Em | Optical Plankton Analyzer | 90–98% | |
Boddy et al. [6] | Back-propagation neural networks, hierarchical approach | 40 | 3–40 µm | 6 | FSC (horizontal, vertical), SSC, TOF 488 nm Ex./> 660 nm 488 nm Ex./530–590 nm | EPICS 741 | > 70% | |
Wilkins et al. [51] | MLP, RBF | 42 | Boddy et al. [6] | 68–74% | ||||
Wilkins et al. [52] | RBF ANN | 34 | 3–1000 µm | 11 | FSC, SSC, TOF 488 nm Ex./Red Em 488 nm Ex./Orange Em 488 nm Ex./Green Em 630 nm Ex./Red Em Diffraction module: Vertical bar, horizontal bar, outer ring, inner ring | EurOPA | 92% | |
Boddy et al. [5] | RBF NN | 72 | 1–45 µm | 7 | FSC-H, SSC-H, FL1-H, FL2-H, FL3-H, FL3-A, TOF | FACSort™ | 70–77% | |
Pulse shape | Malkassian et al. [33] | 20 | n. a. | 8 | FSC, SSC FLR (668–734 nm Em.) FLO (601–668 nm Em.) FLY (536–601 nm Em.) Pulse shape descriptors (shape, length and area under the pulse) | CytoSub | 78% | |
Images | Gorsky et al. [23] | 3 | 3–43 µm | 5 | Area, Circularity, Convexity, Length, Perimeter | Autonomous Image Analyzer/HIAC | – | |
Embleton et al. [17] | MLP | 4 | 10–390 µm | 74 | Area, Circularity, Diameter, Fibrelength, Grey level values (SD, Skewness, Kurtosis), Perimeter | Microscope camera (Sony DXC-930P) | 67–93% | |
Sosik and Olson [45] | SVM | 15 (natural samples) | 10–100 µm | 22 categories/210 elements | Size, shape, symmetry, Texture characteristics, Diffraction, Co-occurence | FlowCytobot | 68–99% | |
Blaschko et al. [4] | SVM | 13 classes | n. a. | 780 features | Simple shape, moments, contour, differential, texture | FlowCam | 71% | |
Correa et al. [9] | CNN | 19 classes | n.a. | – | – | FlowCam | 89% | |
Rodenacker et al. [42] | DT, LDA | 23 | n. a. | 5 | Shape, significant points, principal components, contour, fourier descriptor, extinction, Shape moments, colorimetry, fluorimetry | Inverse microscope | 76% | |
Chen et al. [8] | CNN, PCA + SVM | 1 | n. a. | 16 | Diameter, tight area, perimeter, circularity, major axis, orientation, loose area, median radius, opd, refractive index, absorption, scattering | TS-QPI | < 85% | |
Li et al. [31] | CNN | 9 | n. a. | – | – | Mueller matrix microscope | 97% | |
Pedraza et al. [37] | CNN | 80 | n. a. | – | – | Microscopy | 99% | |
This study | CNN | 9 | 1–90 µm | – | – | ImageStream®X MK II | 97% |