# Classification of Malaria Using Object Detection Models
## 3 Results
### Parasite Classification  
The dataset corresponding to parasite classification was used to train the YOLOv5 and scaled YOLOv4 algorithms. Preliminary results were observed. After that, an alteration to the dataset augmentation was created. Saturation values were varied as an additional augmentation step, and the results are presented in Table 7.  
The YOLOv5 model achieved a mAP of 78%, while the scaled YOLOv4 model achieved a mAP of 83%. It was then concluded that the scaled YOLOv4 model was superior to the YOLOv5 model for this application. Figure 12 provides graphical information about the metrics: mean average precision, precision, and recall for the superior model, and scaled YOLOv4 with the second dataset for stage classification. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **YOLOv5** & **Scaled YOLOv4** \\ \hline Precision & 0.741 & 0.52 \\ Recall & 0.797 & 0.938 \\ mAP & 0.78 & 0.83 \\ Loss & 0.020 & 0.021 \\ Time & 52 min 5 s & 3 h 26 min \\ Epochs & 400 & 400 \\ Image resolution & \(720\times 720\) & \(720\times 720\) \\ Batch size & 16 & 16 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of the results for parasite classification. Figure 11: Stage classification prediction testing by scaled YOLOv4. Figure 12: Metrics for scaled YOLOv4 for parasite classification. Object detection models are not optimized for the detection of microorganisms from microscopic images, as they are minuscule objects. The proposed model can be improvised by adding more detection layers or by enhancing the accuracy of detection. Research published by [PERSON] et al. (2021) worked on this to detect malarial parasites using YOLOv3 and YOLOv4 algorithms (after additional optimization). Their results prove that optimized object detection can be used to detect malarial parasites with an accuracy of 96.32% [29]. Their study focused on binary classification and proposed a pipeline that focused on predicting the presence of only one parasite: plasmodium falciparum rather than a multi class approach as considered in this paper. The algorithms proposed in this paper are also expected to perform exponentially better after model optimization and increasing the feature scale to accurately identify the malarial parasite. Additionally, training the model for a larger number of epochs after optimizing its hyperparameters can also boost its learning capabilities. The parasite classification conducted by the best model, scaled YOLOv4 after testing, is shown in Figure 13.  
Figure 13: Parasite classification by scaled YOLOv4 algorithm after testing. Once again, we can see that the model is able to classify the cells according to the parasite causing the infection highlighted by the bounding box with the name of the parasite. Therefore, it may be concluded that with the right data, the proper stage classification of malaria can be achieved. Parasite classification results also proved satisfactory but can be improved upon further research and development. In a comparison between YOLOv5 and scaled YOLOv4, scaled YOLOv4 proved to be superior in classifying the kind of parasite as well as the stage of progression of the disease. One reason why the YOLOv4 model outperformed the YOLOv5 model may be due to the fact that YOLOv5 suffers from higher latency periods for larger batch sizes. This results in a lower accuracy than that of YOLOv5. This is also taking into consideration that the dataset was of relatively poor resolution and the objects themselves, being red blood cells, were very small. The architecture of YOLOv4 may also have suited the objects of interests, as it has a strong detector module. The future scope of this project would begin with understanding the shortcomings of the dataset and the models used for parasite and stage detection. Further, they can be optimized to function more efficiently and predict the classes of the cells more accurately. This can aid with quicker and correct diagnoses.