## 5 Conclusions and Future Work  
In this study, we have provided an end-to-end machine learning approach for localization of trophozoites and WBC in thick blood smears for the determination of malaria parasite density and integrate the model into a mobile smartphone detection App. We present a low-cost and reliable mobile phone malaria app diagnostic solution that is relevant to highly endemic low resourced settings. We leverage transfer learning and propose a Faster R-CNN ResNet 101 model and SSD MobileNet V2 as the on-device inference approach. All models were pre-trained on COCO dataset for the task of object detection. We show that our proposed models can accomplish parasite and WBC localization and count effectively in a fast, accurate and consistent way for malaria parasitemia determination.  
Furthermore, we evaluated and compared our proposed model based on a multi-class task for detection of both malaria trophozoites and WBCs with performance from single-class tasks for detection of trophozoites (only) and WBCs (only) using the same model. The mAP performance of a multi-class task (0.6609) is better than that of a single class of trophozoite detection (0.5506) and thus a higher precision and recall but lower than that of a single-class of WBCs (0.892). It is expected that models perform better with bigger images, like WBCs, compared to smaller objects (trophozoites only). In fact, SSD MobileNet performed better than Faster R-CNN on bigger objects (WBCs). The better performance of the multi-class task is attributed to having bigger objects (WBCs) complementing small objects (trophozoites). Generally, the Faster R-CNN model for a multi-class task achieved a mAP value of 0.6609 (@0.5 IOU) with F1 score of (parasites: 0.7897 and WBC: 0.8426) providing better model performance in comparison to SSD MobileNet model with a mAP value of 0.6292 (@0.5 IOU) and (parasites: 0.6038 WBC: 0.7528) as expected [32; 41]. SSD provided capacity for mobile smartphone deployment. Conclusively, therefore, different models can be chosen according to the actual needs with respect to accuracy versus low-core device model deployment.  
In future work, we intend to work towards reducing false positives and false negatives generated by the model. This could be through acquiring a bigger dataset with quality data annotation. In its current form, the mobile smartphone app still outputs false alarms. Model improvement is still needed in respect to reduction of false alarms and accuracy improvements.  
R.N. and E.M. designed the study; R.N. implemented the system; [PERSON] and E.M. participated in the study design and supervision; R.N. wrote the manuscript; all authors analyzed the results and helped in proofreading the manuscript. All authors have read and agreed to the published version of the manuscript.  
The first author of this study was funded in part by the Swedish International Development Cooperation Agency (SIDA) and Makerere University under SIDA Contribution No.: 51180060. The grant is part of the European and Developing Countries Clinical Trials Partnership (EDCTP2) programme supported by the European Union.  
The authors thank Ministry of Health, Uganda for providing authorization to use the data. They also acknowledge the laboratory Technicians in Mulago Referral Hospital, Uganda especially [PERSON] and [PERSON] for granting us medical and technical support in data acquisition, annotation and interpretation of results.  
The authors declare that they have no competing interests.