## 2 Related Work  
Pre-processing is an essential step in improving the aesthetics of medical imaging. Many methods exist for pre-processing to render the image noiseless and clean and improve image quality. A multiple image pre-processing technique was used to identify the WBC. They started by converting the RGB color scheme to CMYK. Then, they used contrast stretching to eliminate background noise before adding the Gaussian filter. They then used a color-based clustering strategy for segmentation. Finally, binary Support Vector Machines (SVM) were deployed to categorize the images. In this classifier, the input data is translated into a higher dimensional feature space, and the maximum margin hyperplane was divided into relevant classes [11]. One researcher [20] enhanced image quality by changing rotation (rotated images from the center between 0 and 360 degrees; for data augmentation, adjusting degrees to 12 degrees), illumination, contrast, shearing, horizontal and vertical flip, and translation. Following that, they conditioned a ten-layer convolution neural network CNN architecture. This architecture accomplished two critical tasks. The first task was to extract features from an input image, and the second task was to identify the extracted features using studied CNN features. Several arithmetic operations and methods were established in their work to remove noise and improve image quality. Then, gamma correction and contrast-enhancing techniques are used. Account accuracy relies heavily on segmentation. They used a two-step protocol for image segmentation, one of which is leukocyte localization and the other is f region extraction. They both had three sub-steps. Sub-steps include localization, thresholding three-phase filtration, neighboring cell identification, and cell extraction. At the same time, the nucleus area was extracted by nucleus localization, nucleus isolation, and cytoplasm extraction [21]. To improve image consistency and quality, the researcher converted the original RGB image to Hue-Saturated-Value (HSV) color space. After that, an HSV threshold was multiplied by a Boolean mask of images determined using pixel values. Then, in the blob detection phase, they collected relevant information from leukocytes. Following the pre-processing method, the watershed algorithm was used to segment leukocytes. Later, scientists used the CCN model to remove features. Following that, they classified their dataset using three linear support vector machine models [22]. The procedures of a researcher were examined in two stages. In the first step, they differentiated between red, blue, and green colors in images. In the second step, they transformed images to grayscale. Grayscale images are then filtered for histogram equalization and linear contrast stretching separately. They suggested a three-step segmentation technique, the first of which was WBC extraction, followed by nucleus extraction. The researcher extracted white blood cells using a color segmentation methodology in the first step. Then, in the second stage, they extracted a nucleus using histogram equalization and linear contrast stretching [23]. Another study found that blood cells and their backgrounds differ greatly depending on color and intensity. The primary causes of this inconsistency are lighting variations, camera settings, and staining. As a result, cell segmentation can be made more resilient against such variables by converting the image from RGB to YCbCr. Contrast stretching was performed on the luminance intensity plane (Y channel) to improve details. After applying contrast stretching, the resulting image was found in greyscale, later converted back to YCbCr mode. Finally, the YCbCr color scheme was changed back to the samples' original color scheme (RGB). Diffused-expectations-maximisation (DEM) approach to achieving segmentation requires two thresholds. The Gaussian mixture component determines the area class, and their parameters are approximated using the maximal feasibility approach.