# Staining-Independent Malaria Parasite Detection and Life Stage Classification in Blood Smear Images
## 1 Introduction
Meanwhile, to address the limitations of manual microscopic examination, a cost-saving, accurate, and fast computer diagnostic system is required. There are many reviews for malaria diagnosis and its automated approaches in microscopic images [12; 13; 14; 15]. A deep learning image processing approach has the potential to provide rapid and consistent estimates of examination outcomes. Deep learning has been applied in many medical applications, for example, magnetic resonance imaging (MRI) images [16], computed tomography (CT) images [17], positron emission tomography (PET)/CT images [18], microscopic images [19], fundus images [20], text-based medical data [21], etc. In an early study of automatic malaria examination, morphological methods were considered for red blood cell and malaria parasite detection [22]. In contrast, image processing has shown greater advancements when compared to the manual observation of blood smears by technicians. The introduction of deep learning algorithms has ushered in significant progress in the realm of computer vision, particularly in the detection of abnormalities within medical images. Notably, algorithms such as the YOLO series (V1 [23], V2 [24], V3 [25], V4 [26], V5), SSD [27], and some others, which also refer to one-stage object detection algorithms, approach the object detection process as a unified regression problem rather than proposing regions of interest separately [28]. To aid malaria diagnosis, besides the image processing-based or artificial intelligence-based technologies like what is proposed here, there are some other technologies recently proposed to solve this problem as well. A graphene metasurface biosensor was developed to detect early-stage malaria-infected red blood cells [29]. The bio-impedance signature of infected red blood cells was utilized to detect malaria infection using a capacitive micro-sensor [30]. The differences between electrical impedance spectroscopy of the healthy and the malaria-infected red blood cells were used to distinguish them [31]. The near infrared spectroscopy was applied to diagnose malaria due to _P. falciparum_ and _P. vivax_ through the patients' skin [32]. The malaria-infected red blood cells were detected usingone-dimensional photonic crystals [33]. A combination of a polystyrene-based microfluidic device with an immunoassay and surface enhanced Raman spectroscopy was also applied to the malaria detection problem [34]. Even though these are state-of-the-art methods to detect malaria red blood cells, they are still in the early stages of development. In this research, the automatic methods are proposed to detect and classify infected red blood cells in human blood smear images. Two public benchmark datasets, i.e., the Malaria Bounding Boxes (MBB) dataset and the Malaria Parasite Image Database for Image Processing and Analysis (MP-IDB) dataset, are used for testing detection and classification performances. For the malaria parasite detection problem, the task is to scan each multi-cell image pixel by pixel and find infected cells in that image. This problem is not straightforward because there are also other objects, i.e., non-infected red blood cells, white blood cells, background, debris, etc., in the image. The outputs are the bounded boxes surrounding the infected cells. In this detection stage, only the MBB dataset (_P. vivax_ only) is trained by the proposed methods based on the YOLO algorithm. Meanwhile, the MP-IDB dataset is only utilized as a role of testing to verify how the models trained by _P. vivax_ can only work on datasets of other malaria parasite species. In order to improve the detection performance, five different preprocessing methods are applied. Also, to lessen the impact of Giemsa staining in the training set on the unstained dataset of blood smears, all color images are converted into grayscale before training.