## 2 Method
### Ensemble Deep-Learning Network
The third term extracted spectrum \(DFT\{O(m,n)R^{*}(m,n)\}\), as shown in Equation (3), is centered (the masking method and centering algorithm is introduced in [10] with great detail), and then inverse Fourier transform is performed and get the scaled complex object wavefront \(AO(m,n)\), which is the object wavefront multiplied by the reference wave with amplitude \(A\). Then, a'min-max' normalization algorithm [20] is applied to \(A\). This method of normalization algorithm used in the machine learning  
Figure 2: The structure of **(a)** EDL-IOHC and **(b)** the expansion structure of CNN components and their connection with the concatenate unit. community scales the values in a data array from [minimum value, maximum value] to [\(-1\), \(1\)] through a linear mapping. It normalizes the effect of the scalar multiplication by the reference wave for recognition. The normalization provides a robust pre-processing method for recognition purposes. In the following Figure 3 illustrates the procedures to get the object wavefront (the digital hologram) for training the EDL-IOHC deep learning network. Raw holograms of the tissue are captured from the biological samples. Fast DFT transforms the raw holograms from the spatial domain to the frequency domain. The spectrums of the object wavefronts are extracted, and fast IDFT restores the spectrums into the object wavefronts. The object wavefront extraction methods from raw holograms have been reported in [10; 11; 12; 13] with details. The object wavefront extracted from the above process is a complex quantity that contains both the magnitude and phase components of the object wave \(O(m,n)\), a digital hologram. The full dataset is split into an in-training train set and an out-training test set. The EDL-IOHC is trained with the train set and tested by the test set. It is found that aberrations have come from dust on optical lenses and mirrors, Airy-plaque-like rings [21] out-turn from the system's lenses. However, the deep learning network can adapt to these background irregularities during the first training stage and continue to perform well in the later recognition stage without any necessary background compensation. Ten different types of tissues are captured from ten different types of flawed biological specimens, which are Cucurbita Stem, Pine Stem, Corn (Zea Mays) Seed, House Fly Wing, Honeybee Wing, Bird Feather, Corpus Ventriculi, Liver Section, Lymph Node and Human Chromosome with their class labels shown in Table 1. \begin{table}
\begin{tabular}{c c} \hline \hline
**Specimen Class** & **Class Label** \\ \hline Cucurbita Stem & 0 \\ Pine Stem & 1 \\ Corn (Zea mays) Seed & 2 \\ House Fly Wing & 3 \\ Honeybee Wing & 4 \\ Bird Feather & 5 \\ Corpus Ventriculi & 6 \\ Liver Section & 7 \\ Lymph Node & 8 \\ Human Chromosome & 9 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The class labels for different specimens. Figure 3: The workflow diagram for the processing steps from the biological samples to get the object wavefront. Five hundred raw holograms are captured from tissues of each class of the ten biological specimens and result in a total dataset size of 5000 digital holograms (object wavefront). They are used to train the hologram-classifier EDL-IOHC. Then, the trained hologram-classifier is used to identify the type of biological specimens by recognizing the tissues' digital holograms.