## 1 Introduction  
The advancements in optics and computing technologies have enabled digital holograms of physical three-dimensional (3D) objects to be captured and analyzed at high speed and achieve close to real-time response performance. Holograms can be displayed with a spatial light modulator to reconstruct a visible image and is an ideal solution for recording, storing, and displaying 3D objects in the digital world. However, a hologram comprises high-frequency fringe patterns and is almost impossible to recognize with traditional computer vision methods. Furthermore, in many practical situations, intact extraction of a biological specimen or organ is not feasible, and therefore the object's identity cannot be inferred directly from its outline shape. However, a digital holographic interferometer is an effective hologram-capturing device to examine the microstructure inside a specimen. Furthermore, the off-axis configuration simplifies the difficulties of separating a hologram's zero-order image from the two conjugate virtual and real images in Fourier space. During the early stage of optical hologram classification methods, almost all methods were based on correlation, where a targeted hologram is matched against a library of reference hologram templates. The matching is generally realized with optical correlation and only results in a high matching score if a pair of objects have similar poses and depth. Most of these approaches are sensitive to positional shifts and deformations. The methods and the problems are described in detail by [PERSON] works in [1] and the articles in [2, 3, 4, 5]. The hologram classifier employed in this paper is EDL-IOHC [6], in which a modern deep learning approach is employed. The feature extraction process of the deep learning approach is fully automated. The method solves most of the shift-invariant problems within a single framework with the capability to handle occlusion problems effectively. Nowadays, some researchers are using the common term Hologram interchangeably with the terms 'Raw Hologram' and 'Digital Hologram'. In order to avoid potential confusion, the following definitions explicitly state the difference in their meanings. 'Raw Hologram' is the intensity map recorded on a camera or a film, and usually, this intensity map will be used for the object wavefront reconstruction, as [PERSON] showed in his works in 1948 [7]. 'Digital Hologram' is a representation of the complex optical wavefront diffracted from an object. The complex wavefront is either generated directly by a computer or reconstructed digitally from a 'Raw Hologram'. A computer-generated hologram is often referred to as wavefront-based CGH [8], Computer Generated Hologram, starting from the works by [PERSON] and [PERSON] [9] during the period of the mid-1960s. A basic tool in two-dimensional digital signal processing used in digital holography is DFT (discrete Fourier transform) and the corresponding IDFT (inverse discrete Fourier transform), making fast computation possible. For raw holograms captured by a modern digital camera, their corresponding frequency spectrums are also discrete. The DFT transforms the raw hologram from the spatial domain to the frequency domain in a discrete manner. The raw hologram spectrum and the corresponding object wavefront extraction methods have been reported in detail in numerous works in the literature, such as [10; 11; 12; 13]. In the frequency domain of a raw hologram, the spectrums of the zero-order image, virtual image, and real image are shifted and separated by the off-axis configuration. As a result, the object wavefront (digital hologram) can be extracted relatively easily. In this paper, we have proposed a novel technique for identifying biological samples based on their tissues' digital holograms. The paper is organized as follows.