# Staining-Independent Malaria Parasite Detection and Life Stage Classification in Blood Smear Images
## 5 Discussion  
The proposed detection method was compared to other existing works, it was found that the proposed method achieved comparable performance to them. To be more specific, while the proposed detection method achieved 0.92 in accuracy on the MBB dataset, the detection performances of previous works ranged from 0.53 average precision (AP) using the mask R-CNN with ResNet101 as the backbone [50], 0.939 accuracy using the integrated CNN [51], to 0.997 accuracy using a semantic segmentation CNN [52]. It is worthwhile noting that it is not really possible to compare the results directly because different approaches used different setups. The results were on different subsets of test data, and their generalizations are always questionable. Many methods might perform extremely well only on a particular dataset or subset. As shown here, the proposed detection method was able to be further applied efficiently to another dataset (MP-IDB) even with other plasmodium species. Also, for the malaria parasite life stage classification, the performances of the proposed methods are compared to results achieved by the previous works. Unfortunately, there are only a few works that worked on malaria parasite life stage classification. Most of the previous works were on species classification, like that in [53; 54; 55], which is different from the current problem of interest. There is a study on malaria parasite life stage classification on a different dataset that achieved 0.588 accuracy using a random forest classifier [56]. For the works on malaria parasite life stage classification on the MP-IDB dataset, they did not report the detailed results for each species. Some of them considered only subsets of some species of the MP-IDB dataset. The classification performances achieved by different methods [57; 58; 59; 60; 61; 62] on the MP-IDB dataset for different plasmodium species, including _P. vivax_, _P. ovale_, _P. malariae_, _P. falciparum_, or some subsets of it, are shown in Table 10. Even though it is difficult to compare the results from different works because they used different ways of reporting the results, to provide clearer comparison through visualization, the plots of related results that could be compared are shown in Figures 7-9. In Figure 7, the precision and recall of life stage classification on the MBB dataset, which contains only _P. vivax_, are shown. It can be clearly seen that the proposed method performed better than its counterparts. The life stage classification accuracies on the MP-IDB dataset when considering only _P. falciparum_ are shown in Figure 8. Once again, it can be seen that the proposed method performed better than previous works that used the AlexNet, GoogleNet, ResNet-101, DenseNet-201, and VGG-16. When all four species and all four life stages are considered, the average accuracy, average precision, and average recall are exploited for comparison. It should be noted that the averages are calculated across four life stages for the results from [62] and across four species for the results from the proposed method. It is believed that the average evaluation measures would be good representatives to evaluate the classification performance when the entire dataset is considered. The average accuracy, average precision, and average recall are shown in Figure 9. The figure shows that the three methods achieve similar average accuracy, in which the Darknet53 might yield a little bit better performance. However, it is clear that the proposed method yielded better average precision and average recall than the VGG-16 and Darket53. It can be seen from Table 10 and Figures 7-9 that the proposed malaria parasite life stage classification method achieved similar or better results compared to its counterparts. As mentioned in the detection performance comparison, the resultscannot be compared directly because of different setups in different works. Some extremely good performance achieved by some methods might not be applied in general.