# Classification of Malaria Using Object Detection Models
## 4 Conclusions  
Presently, many researchers focus on deep learning for the detection of malaria. However, these techniques vary from traditional segmentation or morphological operations to those that employ cutting edge deep learning or machine learning techniques. In this article, we explored the use of object detection techniques for the classification of the type of malarial parasite and the stage of progression of the disease. For the stage task, the dataset employed contained 1330 images of microscopic blood smears from patients infected with malaria. The label information for the images was extracted from the corresponding csv file, and the images were manually annotated using the Labellmg tool and then uploaded to the 'Robolflow' website for pre-processing and augmentation. The YOLOv5 and scaled YOLOv4 object detection models were then trained on this dataset. When this returned unsatisfactory results, changes were made to the pre-processing pipeline and the training pipeline to observe any significant changes in the training accuracy. This also returned unsatisfactory results, and thus the dataset was discarded. It is suspected that these data were unsuitable due to the large imbalances between the classes. Many were underrepresented, while few were vastly overrepresented. Thus, a second dataset was annotated (again using Labellmg). The data were uploaded to Roboflow and underwent pre-processing and augmentation, and then were used to train the models. By comparing the results obtained, we can conclude that the scaled YOLOv4 model is superior to the YOLOv5 model, scoring a mean average precision of 68.4% and is considered relevant to the research. For the parasite classification part of the problem, the revised dataset used for stage classification was again annotated according to the classes fitting this task, using Labellmg, and then uploaded to Roboflow for pre-processing and augmentation. Then the data were used to train the YOLOv5 and scaled YOLOv4 models. On comparing the two models in the given reported study, it was observed that the scaled YOLOv4 model was better than the YOLOv5 model, achieving mean average precisions of 83% and 78%, respectively. The results of these models, while significant, can be improved in terms of mAP, precision, and recall. The future scope of this research lies in improving the object detection models by adding depth to the model architecture and/or incorporating feature scaling to better suit tiny object detection as required for small objects, such as infected red blood cells in microscopic slides. The authors strongly believe that with the implementations of these improvements, the model can be deployed in real-world situations to assist clinicians in delivering accurate, fast and reliable treatment not only at hospitals, but also to rural areas of the world that lack access to healthcare resources. This can also help advance the field of telemedicine and promote affordable, accessible, and safe healthcare for everyone who needs it. P.K. : Software, Draft preparation; K.C. : formal analysis; N.S. : Supervision and project administration; S.R. : Draft editing; S.K.S. : Project administration and draft editing; S.P. Methodology, Draft editing. All authors have read and agreed to the published version of the manuscript. This research received no external funding. **Institutional Review Board Statement:** Not applicable. Informed Consent Statement: Not applicable. **Data Availability Statement:** The dataset that was considered for the stage classification task is the Mendeley dataset taken from the paper \"[PERSON], [PERSON] & [PERSON] Annotated high-throughput microscopy image sets for validation. _Nat Methods_**9**, 637 (2012). [[https://doi.org/10.1038/nmeth.2083](https://doi.org/10.1038/nmeth.2083)]([https://doi.org/10.1038/nmeth.2083](https://doi.org/10.1038/nmeth.2083)).\" (27). The dataset for parasite classification is from \"[PERSON], [PERSON] [PERSON], [PERSON] and [PERSON] [PERSON], 2018, September.