# Classification of Malaria Using Object Detection Models
###### Abstract  
Malaria poses a global health problem every day, as it affects millions of lives all over the world. A traditional diagnosis requires the manual inspection of blood smears from the patient under a microscope to check for the malaria parasite. This is often time consuming and subject to error. Thus, the automated detection and classification of the malaria type and stage of progression can provide a quicker and more accurate diagnosis for patients. In this research, we used two object detection models, YOLOv5 and scaled YOLOv4, to classify the stage of progression and type of malaria parasite. We also used two different datasets for the classification of stage and parasite type while assessing the viability of the dataset for the task. The dataset used is comprised of microscopic images of red blood cells that were either parasitized or uninfected. The infected cells were classified based on two broad categories: the type of malarial parasite causing the infection and the stage of progression of the disease. The dataset was manually annotated using the Labellmg tool. The images were then augmented to enhance model training. Both models YOLOv5 and scaled YOLOv4 proved effective in classifying the type of parasite. Scaled YOLOv4 was in the lead with an accuracy of 83% followed by YOLOv5 with an accuracy of 78.5%. The proposed models may be useful for the medical professionals in the accurate diagnosis of malaria and its stage prediction.  
object detection; malaria detection; automation; YOLOv5; scaled YOLOv4 2022  
laboratory facilities [2]. The blood sample is placed on a sample pad on a test card along with some reagents. After 15 min, the test cards show specific bands that indicate if the patient is infected with malaria and the type of species. Another method for malaria detection is the indirect fluorescent antibody test (serology test), which detects the malaria antibody in the blood. It can be used to detect if the patient is infected with plasmodium content. In this method, schizons are used as the antigen. The patient's serum is attached to the homologous antibody (the organism), and if they are present, they attach to the antigen. This, when examined under a fluorescent microscope, gives the parasite a fluorescent green color [3]. However, these tests are time consuming and are not practical for day-to-day testing. There are also spectroscopic methods of testing for malaria. Raman spectroscopy uses chemical fingerprints to determine if the patient is infected with malaria [4]. Generally, the disease is treated in its early stages before it becomes fatal to the infected individual. Malaria can typically be treated with a range of medications depending on the type of malaria parasite that has caused the infection, the severity of the illness, and the person's age, general immunity, and gender.  
However, the screening and diagnosis of malaria is not an easy task. The identification of malaria parasites in a blood sample using a microscope is considered a gold standard for laboratory confirmation of malaria, but this is a tedious process. The rapid diagnostic tests have their disadvantages as well, as they do not eradicate the need for microscopic confirmation. They also have insufficient data to determine the ability of the test to detect all species of malaria accurately. Microscopic analyses are time consuming and lead to inaccuracy and inconsistency in some cases. The computerized approach uses digitized blood slides that are able to improve the consistency in diagnosis [5]. In this study, we propose a method for the automated classification of malarial parasites and the stage of progression of the disease from Giemsa-stained blood smears obtained from the patients. We use object detection algorithms, YOLOv5 and scaled YOLOv4, to obtain predictions of the class of the parasitic infection. The two models are also compared to understand their efficiency and accuracy in carrying out the task. Further, all the models are implemented using the PyTorch framework.