# Staining-Independent Malaria Parasite Detection and Life Stage Classification in Blood Smear Images
## 4 Experimental Results
falciparum_ reached 0.92 with the largest number of 104 multi-cell images. While considering _P. ovale_, the detection performances were reasonably high. The accuracy reached 0.82, ranked 3 rd among the 4 species. However, with the number of 29 multi-cell images, which is the lowest among the species, the small sample size may affect the reliability of the results. For _P. malariae_, which contains 37 multi-cell images, it yielded the lowest detection performance among them. It suggested that increasing the dataset size might enhance the detection performance for this species. Of course, another possible reason may be that the images of this species itself contain a large number of overlapping cells, resulting in a much more difficult malaria parasite detection problem. Therefore, collecting related and high-quality data might be a key to improving the detection performance. Another point worth noting is that since the training malaria parasite detection model was trained only on the MBB dataset (_P. vivax_ species only), it was a bit surprising that the _P. vivax_ test images did not become the ones with the highest accuracy. Due to the model being trained solely on _P. vivax_ images, it could be speculated that the feature representations learned from the model were more appropriate for _P. vivax_. However, the precision, recall, F1-score, and accuracy achieved by the trained model on _P. falciparum_ were exceptional. This might suggest that _P. falciparum_ shared more distinguishable features with _P. vivax_ or that the dataset used for evaluation had a more representative sample of _P. falciparum_. Conversely, the model struggled with _P. malariae_, suggesting that _P. malariae_ might have more subtle differences from _P. vivax_ or less distinct features that the model could not extend from the _P. vivax_ data alone. The ability that this detection model was generalized from the _P. vivax_ data to other species like _P. falciparum_, _P. ovale_, or _P. malariae_ suggested some level of transferable feature learning or generalization ability. As mentioned earlier, however, the poor detection performance on _P. malariae_ highlights the need for more diverse training data to improve its detection performance. Thus, it shows the potential to diversify training data is worth trying again in the future. For malaria parasite stage of life classification, all classification models trained and tested on single-cell images with the 3 different cropping methods yielded quite good classification performance. They achieved accuracies of around 0.90. By comparing the results from Tables 5-9, it can be found that the direct cropping method is generally the best-performing method, especially for _P. vivax_ and _P. malariae_ species. It demonstrated the highest overall accuracies and F1-scores in most of the 5 scenarios, i.e., MBB (_P. vivax_), MP-IDB (_P. vivax_), MP-IDB (_P. ovale_), MP-IDB (_P. malariae_), and MP-IDB (_P. falciparum_). This cropping method performed well on the _P. vivax_ data, especially for red blood cells and schizont stage among all 5 scenarios. The classification of red blood cells yielded very high precision, recall, and F1-score (close to 1.00 in most cases). The performances might not be that good in ring and trophozoite classes in the MP-IDB (_P. falciparum_) data. However, the F1-scores of 0.78 and 0.85 were still quite good. The zero padding method still performed reasonably well but yielded lower accuracies and F1-scores for certain classes, particularly for trophozoites and schizonts classes. It also provided high performance for red blood cell classification and achieved perfect or near-perfect precision and recall. Ityielded slightly lower overall accuracy compared to the direct cropping and cropping with background methods. Meanwhile, compared to the direct cropping method, the cropping with background method often performed particularly well on the _P. falciparum_ data with the highest accuracy of 0.96. It also showed a good balance between different malaria parasite species and stages.