## 2 Related Work  
The traditional pipeline of automating the process of malaria diagnosis follows four steps: image pre-processing, cell segmentation, feature selection, and classification of infected and uninfected malaria cells, as shown in Figure 1. For each step, different methods are proposed in the literature. Image pre-processing methods enhance the quality of blood smear images to improve the accuracy of later processing steps such as cell segmentation, feature extraction, and classification. Any kind of impurities in the images can affect the performance of the later processing steps and are vulnerable to the misclassification of malaria cells. Different smoothing filters, e.g., Gaussian, median, and geometric mean filters, are extensively used to suppress noise in microscopic images [11; 12]. Morphological operators have also been used to remove impurities by improving cell contours and suppressing noise by filling holes [13]. Adaptive threshold and histogram equalization is also used to enhance the resolution and contrast of the images [14]. Malaria detection methods e.g., [15] used HSV color space and grayscale color normalization to reduce illumination variation in cells. Low-pass filters have also been exploited to eliminate noise-related frequency components from the microscopic images [16]. Some techniques e.g., [17] used the Laplacian filter to sharpen edges and to enhance the red blood cell (RBC) boundaries in images. The method in [18] used the Wiener filter to remove the blurriness induced in the microscopic images due to unfocussed optics. Cell segmentation is the most significant step in any automated malaria detection system. Pre-processed microscopic images are segmented into small non-overlapping regions containing the red blood cells (RBC), the white blood cells (WBC), the malaria parasites, and other artifacts. Image-based methods such as Chan-Vese segmentation, hole-filling algorithms, and histogram-method-based methods are popular for cell segmentation in an unsupervised manner [19]. A green channel of RGB images is used to segment cells in case of low-contrast images in [20]. In [21], the Otsu threshold is used to segment RBCs from enhanced images. In malaria detectors such as [22; 23], thresholding techniques are applied in Hue-Saturation-Value (HSV) color space on S and V channels to segment cells from microscopic images. The fuzzy divergence technique is used for cell segmentation in [24]. The fuzzy rule-based segmentation method is applied in [25] to segment malaria cells from images of three different color spaces. In [13], morphological-based approaches use grayscale granulometry to capture regional extrema and segment cells. Hough transform is used in [26] to identify RBC using its shape, and k-means clustering is used to segment cells from unlabeled data in [27]. Marker controlled watershed algorithm is used in [28; 29] to separate the overlapping cells that complicate the segmentation process. A graph-cuts based technique for cell segmentation is proposed in [30]. The methods in [31; 32] utilize  
Figure 1: Traditional automated malaria detection pipeline. the structure, geometry, and color information of cells to identify WBC and gametocytes. Machine learning approaches and neural networks are used for cell segmentation in [33]. Feature selection for cell images depends on the shape, texture, and color of red blood cells. HSV color space and green channel of RGB color space are preferred for feature extraction because the color features are prominent in stained blood images [31; 34]. Histogram of oriented gradients (HOG) features [26], Haralick's texture features [35], local binary patterns [36], and various other feature-selection methods [37] have been used to extract features from cell images. Types of parasite can be identified from cell images by using the color and shape information [38]. Morphological operations like thinning and grayscale granulometry capture relevant information from the intensity of image pixels [39; 40].