# Classification of Malaria Using Object Detection Models
## 3 Results
### Parasite Classification
Another vital area that could help improve not only the results of this study, but the entire field of digital pathology is increasing the amount of public data available for research and development. Artificial intelligence algorithms rely heavily on the quality and the quantity of the data used, and hence a vast database can be highly beneficial to both employees of the healthcare industry as well as the patients. The accuracy and performance of the models can also be improved by further pre-processing the images before beginning the training process. Various cell segmentation processes could be applied to assist better model learning [30; 31]. It Is worth mentioning that cell segmentation process should not be mistaken for segmentation algorithms, such as U-Net that have been extensively researched upon [11]. [PERSON] et al. reported an accuracy of 92.06% on employing U-Net for cell segmentation. These results, while impressive, should not be compared to results from models employing CNNs or object detection algorithms. This is because the objective functions of the two models are distinct, and while segmentation algorithms focus on classifying each pixel of the image into respective classes, object detection algorithms focus on classifying patches of the image rather than pixels into their respective classes. A direct consequence is the requirement of different metrics and loss functions to measure the quality of the two models, thus rendering the results incomparable at face value. Other image processing methods, such as feature scaling, may also be employed to facilitate better feature extraction and can significantly increase the learning ability of the models used [5; 32; 33]. Furthermore, models can be developed to ascertain the concentration of the parasite in the blood smear to better understand the rapid progression of the disease and make an appropriate diagnosis [33]. Optimized models that meet the required specifications can then be integrated with a microscope. The microscope is used to take the picture of the blood smear and then run it through the model to identify the parasite causing the infection and the stage of progression of the disease. This can be a great tool, as early treatment is a crucial factor for the recovery of the patient due to the rapidly progressive nature of the disease [34]. The authors would like to note that in the case of real-world deployment, the use of tools such as Labellmg for annotation and the Roboflow platform for augmentation is purely restricted to the data pre-processing pipeline, and they will not be required during deployment. They are only used to process the dataset used to train the optimized model. Once the model undergoes training, validation and testing and achieves satisfactory results, only the classification model will be deployed to detect and classify the malaria parasites. The algorithms can also be used in telemedicine, where blood smears of patients in rural or places far from a laboratory are collected [35]. The images may then be sent to the concerned individuals and run through the algorithm. Thus, it can be used to treat patients in rural areas with poor resources and help change and better a pathologist's approach to diagnosis [36; 37]. Applications can also be built, such as portable impedance analyzers for rapid screening tests [38]. The approaches discussed in this project as well as the previous work conducted in the area cover a variety of model architectures, each with different objective functions. They are all, however, focused on a common goal: computer-aided malaria detection, or CAM detection. These CAM detection approaches offer a new perspective or second opinion to doctors [39; 40]. Ranging from clustering algorithms to segmentation algorithms, there is great potential in computer-aided detection, and this can be the next revolutionary step in the healthcare industry.