## 2 Related Work  
Previous studies have attempted to address the problem of parasitemia estimation for example [PERSON] et al. (2018) presented a software, MalariaCount, that automatically generates parasitemias from images of thin Giemsa-stained blood smears. In a subsequent effort, [PERSON] and [PERSON] (2018), presented a technique for determining malaria parasitemia based on a count of Red Blood Cells (RBCs) through a classification task of parasitized and non parasitized cells. They both applied the Otsu thresholding technique on gray image and green channel of the blood image for cell segmentation. Here, watershed-transform was used for separation of touching cells and color. Statistical features were then extracted from segmented cells and SVM binary classifier was used to classification of normal and infected cells. [PERSON] et al. (2018) proposed an end-to-end automated detection system for identifying and quantifying malaria parasites (P. falciparum) in thin blood smears of both human and mouse. The authors use a combination of color and texture features to characterize segmented RBCs and a linear Support Vector Machine (SVM) model to classify infected and uninfected cells. These studies have mainly focused on thin blood smear images ([PERSON] et al., 2018). However, according to WHO, thick blood films are the gold standard for the determination of malaria parasitemia ([PERSON] et al., 2018) and provide better detection of parasites than thin blood smears ([PERSON] et al., 2018). Thick blood smears contain several layers of red cells, whereas thin films contain a single layer of spread red blood cells. Therefore, for a fixed number of microscope fields, thick films allow the microscopist to examine a larger number of red cells for the presence of parasites, and consequently low parasitemia can be readily identified in thick films ([PERSON] et al., 2018). [PERSON] (2018) used a semi-automated method for parasitemia determination in thick smears by using an open source java based image processing program ImageJ (version 1.41) for image analysis. In essence, the program segments or classifies particles to be counted on the basis of their relative density (darkness) compared with the background via a thresholding process. For this study, conventional statistical evaluations were done using Statistica 8.0 ([PERSON] et al., 2018). The image analysis software used was based on morphological feature extraction with no detection specificity reported. The authors enumerated parasites per image manually by counting parasites on the captured images. Currently, deep learning models come with an added advantage in that they do not require expertise for the hand engineered features ([PERSON] et al., 2018). They have, therefore, aided development of systems for medical image analysis [20]. [PERSON] et al. [5] developed a smartphone application based on intensity-based Iterative Global Minimum Screening (IGMS). The researchers use this method for automatic pre-selection of malaria parasites in thick blood smears and a customized Convolutional Neural Network (CNN) classification model of the parasite. In a separate experiment, the authors localized WBCs using segmentation. However, Customized CNN models have been associated with back and forth fine-tuning of the model layers and huge volumes of annotated data for improved accuracy which may not be available for medical image analysis [6; 21]. Moreover, the authors implemented a single-class detection task for mobile deployment. As opposed to semi-automated and customized CNN models for classification of tasks on malaria parasitemia, this study presents an end-to-end automated pre-trained deep learning approach for detection and counting of malaria parasites and WBCs for improved parasitemia determination in thick blood smears. Unlike the previous study that focused on a single class object detection task for malaria parasites in thick blood smear [22], the pre-trained deep learning approach utilized in this study is a multi-class detection task for parasites and WBCs.