# Classification of Malaria Using Object Detection Models
### Literature Review  
There is a wide array of research conducted to automate and diagnose malaria from the microscopic images of blood smears acquired systematically from the peripheral blood smear using deep learning. The important research which focuses on the automation of malaria detection is discussed here. [PERSON] et al. (2018) reported a computational approach to the diagnosis of malaria through the classification of the malaria parasite from the microscopic image of a blood  
Figure 2: Malaria parasites and stages of progression. smear. It illustrates the usage of image segmentation and feature extraction to classify malaria present in images of microscopic blood smears. The hue, saturation, and value color space of each image were analyzed to remove unwanted noise or objects from the image. The data were then passed through a neural network to undergo feature classification and extraction based on colors, texture, and other desired features. The research achieved a training accuracy of 97.2% [5]. [PERSON] et al. (2020) developed a fully automated system to detect malaria parasites and their stages from the blood smear that illustrates the usage of one of the first deep learning algorithms. VGG16 was utilized to identify infected RBC parasites. It performed the automatic detection of malaria along with its stages. They achieved a segmentation accuracy of 97.67%. The U-Net model achieved an accuracy of 92.05%. The CNN (convolutional neural network) model also obtained a detection accuracy of 100% and a specificity of 95%. The average accuracy and specificity of the VGG16 model were 95.55% and 94.75%, respectively [11]. [PERSON] et al. (2019) investigated different deep learning models and their efficiency in detecting malaria. Their results showed the ResNet 50 model to be extremely promising with the highest training accuracy of 97.55%. [12]. The author assessed the performance of prominent deep learning models, such as ResNet50, AlexNet, FastAI V1, VGG-16 and DenseNet121. [PERSON] et al. (2018) detected the malaria parasite in the Giesma blood sample using image processing, where they developed a model that used the color pixel-based discrimination method and a segmentation operation to identify malarial parasites in microscopic blood smear images [13]. Their methodology involved using two different segmentations: watershed segmentation and HSV (hue, saturation, and value space) segmentation. Then they followed the morphological operations to highlight the presence of the parasite in the microscopic images of RBCs. Their methods resulted in a 90% accuracy in the detection of the parasite causing the disease. [PERSON] et al. (2016) proposed a method to analyze malaria using mobile phone imaging and cloud-based analysis for standardized malaria detection, where a mobile phone was used to take images for conducting a rapid diagnostic test. It also enabled the objective recording of the rapid diagnostic test, and this enabled web access for immediate result reporting. These images were uploaded to a database that was globally accessible and then analyzed. It achieved an 80.2% true negative rate (specificity). This was a novel use of digital pathology to ensure top-notch healthcare for patients [14]. More recent approaches to malaria detection can be observed in the paper put forth by authors [PERSON] et al. (2022) in the paper \"Residual Attention learning network and SVM for malaria parasite detection\". The authors explored a hybrid model called RAL-CNN-SVM composed of multiple residual attention learning network modules, a global average pooling block and a classifier trained by a support vector machine. The model was observed to improve prediction accuracy significantly without involving additional complex computations [15]. [PERSON] et al. (2021) discussed the evolution of the YOLO algorithm and YOLOv5 as a state-of-the-art object detection algorithm [16].