## 1 Introduction  
Malaria is still a global health concern with nearly half of the world's population at risk. Two hundred and twenty-eight million cases were reported in 2018, and the estimated number of malaria deaths stood at 405,000 [1]. These statistics are majorly a reflection of cases in sub Saharan Africa, but other regions, including South-East Asia, Eastern Mediterranean, Western Pacific, and United States of America, are also at risk. When diagnosing malaria, the quantitative content of malaria parasites in the blood also referred to as malaria parasitemia is key as it plays a pivotal role in decision support for clinicians with respect to the severity of the malaria, choice of treatment, and probable cause of the disease [2]. However, in highly endemic but low resourced regions, like sub-Saharan Africa, there is lack of sufficient skilled human capacity to efficiently determine malaria parasitemia density of patients. The situation is not made any better by the existing conventional methods for parasitemia determination, like microscopy. This de facto gold standard method for malaria detection and parasitemia determination in most endemic countries involves the manual count of parasites (trophozoites) and White Blood Cells (WBCs) (see in Figure 1) by expert Microscopists [3] (as shown in Figure 1). Counting malaria parasites and WBCs by microscopy method is not only laborious and time consuming but also susceptible to subjectivity by Microscopists [4]. In view of the above microscopy challenges, this research sought to develop an efficient computer vision automated system for identification and quantification of malaria parasitemia and couple the solution with a real time mobile smart phone localization app for malaria diagnosis. Malaria diagnosis automation using a mobile smart phone app for parasitemia determination improves reliability and exactness in interpreting malaria disease severity. It also increases throughput especially in highly endemic but low resourced developing countries. Current advancements in technologies have made it possible to change the way microscopy of malaria is performed in developing countries. New technologies, like deep learning, can be leveraged with a combination of smart phones to improve disease diagnosis. Given that mobile smartphones are widely owned across the developing world, there is a technological opportunity to address malaria microscopy. Mobile smart phones are now equipped with digital cameras and increased computational power which affords them the potential to capture and process microscopy images. This process is reinforced using pre-trained deep learning models based on transfer learning. Since medical image datasets are usually smaller in size often termed inadequate for learning [6; 7], its important to use pre-trained deep learning as a way of utilizing small datasets to produce results that would otherwise be produced by a big dataset. Specifically, transfer learning focuses on sharing knowledge gained while solving one task and applying it to a different but related problem [8]. The learned features can serve as initialization for the another model. In transfer learning, representations learned on a large dataset can be transferred to a model with a totally different yet small data domain [9; 10]. Transferring such knowledge from already trained models using big datasets makes computational training of small datasets faster and efficient. Since in this study we use a smaller dataset, the would be challenges are hence cured by using the deep learning models where our small dataset is trained through transfer learning by pre-trained models on already existing big datasets, such as Common Objects in Context (COCO) [11]. In this study, therefore, we propose the use of novel pre-trained deep learning models for a multi-class detection task and Mobile smart phone app for automated localization and quantification of malaria density in thick blood smears.