# Staining-Independent Malaria Parasite Detection and Life Stage Classification in Blood Smear Images
## 5 Discussion
The bottom line is that the proposed malaria parasite detection and stage of life classification methods are very promising and achieved comparable results to the existing methods. However, the image preprocessing methods that convert each input color image into a grayscale version make the proposed methods robust to variation in staining methods. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Dataset** & **Classification Performance** \\ \hline Scaled YOLOv4 [57] & MBB & _P. vivax_: precision = 0.37, recall = 0.86 \\ YOLOv5 [57] & MBB & _P. vivax_: precision = 0.45, recall = 0.56 \\ Proposed method & MBB & _P. vivax_: accuracy = 0.93, precision = 0.92, recall = 0.92 \\ \hline AlexNet [58] & MP-IDB & _P. falciparum_ only: accuracy = 0.90 \\ GoogleNet [58] & MP-IDB & _P. falciparum_ only: accuracy = 0.93 \\ ResNet-101 [58] & MP-IDB & _P. falciparum_ only: accuracy = 0.95 \\ DenseNet-201 [58] & MP-IDB & _P. falciparum_ only: accuracy = 0.94 \\ VGG-16 [58] & MP-IDB & _P. falciparum_ only: accuracy = 0.94 \\ CapsNet [59] & MP-IDB & _P. vivax_ and _P. falciparum_ (no species-wise results): accuracy = 0.97 \\ VGG-16 [60] & MP-IDB & Four species (no results on each species): accuracy = 0.96 \\ MobileNet V1 [61] & MP-IDB & Four species (no results on each species): accuracy = 0.99 \\  & & Ring: accuracy = 0.94, precision = 0.97, recall = 0.96 \\  & & Trophozoite: accuracy = 0.92, precision = 0.57, recall = 0.44 \\ VGG-16 [62] & MP-IDB & Schizont: accuracy = 0.95, precision = 0.22, recall = 0.44 \\  & & Gametocyte: accuracy = 0.97, precision = 0.57, recall = 0.80 \\  & & Ring: accuracy = 0.99, precision = 0.99, recall = 0.99 \\ Darknet53 [62] & MP-IDB & Trophozoite: accuracy = 0.98, precision = 0.71, recall = 0.62 \\  & & Schizont: accuracy = 0.98, precision = 0.67, recall = 0.67 \\  & & Gametocyte: accuracy = 0.99, precision = 0.83, recall = 1.00 \\  & & _P. vivax_: accuracy = 0.95, precision = 0.96, recall = 0.95 \\ Proposed method & MP-IDB & _P. ovalie_: accuracy = 0.93, precision = 0.93, recall = 0.93 \\  & & _P. malariae_: accuracy = 0.94, precision = 0.94, recall = 0.94 \\  & & _P. falciparum_: accuracy = 0.96, precision = 0.95, recall = 0.94 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparison of malaria parasite life stage classification results achieved by different methods on MP-IDB dataset for different plasmodium species. Figure 7: Comparison of precision and recall of life stage classification achieved by different works including the scaled YOLOv4 [57], YOLOv5 [57], and the proposed method, on the MBB dataset. The computational complexity of the YOLO algorithms and CNN algorithms has been improved all the time. If different methods utilize the same algorithm, then their complexities are not much different. In real-world applications, a factor that certainly imposes the difference in computation time is the input image size. Even with the same dataset of images, the use of higher resolution images renders a larger number of pixels, which would force a longer computation time. Due to different experimental settings in different methods or different experimental settings even within one method, it is hard to compare them based on the actual computation time. Moreover, the systems used in the experiments are the crucial factor. The high-end cloud computing systems would definitely yield shorter computation times than personal computers or lower end cloud systems. In this research, the YOLO and CNN algorithms are utilized because they provide fast computation among the deep learning-based algorithms. In the experiments, a cloud computing service from Tencent Cloud was utilized. The information about the service is as  
Figure 8: Comparison of accuracy of life stage classification achieved by different works including AlexNet [58], GoogleNet [58], ResNet-101 [58], DenseNet-201 [58], VGG-16 [58], and the proposed method, on the MP-IDB dataset when only _P. falciparum_ is considered.