# Staining-Independent Malaria Parasite Detection and Life Stage Classification in Blood Smear Images
## 6 Limitations and Future Directions  
For future work, combining the strengths of direct cropping and cropping with background methods could be explored to create a more robust method. Furthermore, there are still a lot of parameters in many steps in the experiments that can be optimized. In addition, besides the YOLOv5 and CNN LeNet-5 algorithms used in this research, some other more new algorithms can be used as well, for example, YOLOv8 in malaria parasite detection and VGG-16 in life stage classification [63]. Except for the YOLO series, the other complicated algorithms can also be used to improve the detection results. It can be seen from the classification results that the classification performances of some malaria parasite species can still be improved, especially schizont and trophozoite. Even though data augmentation had been performed, these two classes still yielded the lowest classification performances among other species. Although this work has proved that the models based on the YOLO-based models trained by _P. vivax_ solely worked for different species of malaria parasite detection, due to the relatively small number of images included in both datasets, continued data collection is imperative. Especially for machine learning methods, a lack of sufficient data support can lead to unreliable results [64]. Furthermore, for future research, the collection and expansion of datasets cannot only be to expand scholar and trophozoite stages but also to collect more data of _P. falciparum_, _P. malariae_, _P. ovale_, _P. vivax_, and _P. knowlesi_ species with some other staining method [65]. Then, in this way, the algorithm may not only detect whether a cell is infected or not but can also distinguish the plasmodium species. For an implementation issue, during the training process for malaria parasite life stage classification by CNN LeNet-5 models on the MBB dataset, there was a difficulty arising from the lack of GPU memory. It was unfortunate that the MBB dataset after image augmentation, containing about 400,000 images, was too large to be trained. It was almost impossible to train the dataset directly. The CNNs could only be trained after size reduction via image downsampling. Even though the downsampled images still looked visually acceptable, some image information will be lost, such as blurring or generating artifacts [66]. Thus, it is believed that if the image size was not reduced, the training performance of the MBB dataset should be better. As mentioned earlier, the proposed methods were executed online using a commercial cloud computing service and achieved fast analysis, i.e., approximately 4 s to analyze each multi-cell image for the detection problem and approximately 10 milliseconds to analyze each single-cell image for the classification problem. This approach imposes a limitation that internet connections are required. In the scenario where no internet connection is available, one or more high-performance computers are required on-site to achieve the real-time analysis. Fortunately, malaria screening does not require such a real-time capability, i.e., completing the execution in microseconds or milliseconds. So, this limitation is not very crucial in this problem. Besides plasmodium parasite detection and the parasite stage of life classification, which are the main themes of this work, there were also some other efforts proposed to apply YOLO algorithms or their variations to automated malaria diagnosis problems. It is interesting that there are several different aspects to consider problems in the same common problem of malaria diagnosis. To a low-resource setting utilization, the YOLOv3 algorithm was applied to detect malaria in images taken from a mobile phone camera [67]. Considering the variation in parasite size, the YOLOv8 algorithm was utilized, focusing on detecting several parasite sizes [68]. The YOLOv4 algorithm was modified to reduce the computational complexity via layer pruning [69].