## 4 Results  
Results from our training experiments show that initially, mAP increases fast and reaches an optimum for the number of steps different for each model as indicated in Table 2. Figures 3 and 4 present the graphical representation of the resultant mAP and loss curves of Faster R-CNN and SSD MobileNet. Figures 3 and 4 show that Faster R-CNN model achieves better accuracy (in terms of mAP and F1 score) than SSD model for localizing both parasites and WBCs as expected [41]. To visualize the results, model performance inference detections on a sample test image are shown in Figure 5. Again, We observed from the depiction that Faster R-CNN (b) performs better than SDD MobileNet model (SSD (c)). A general representation of our experimental results is summarized in Table 2.  
Table 2 shows that a multi-class detection task of both malaria trophozoites (parasites) and WBCs produces better results than a single-class task (trophozoites only), but lower than a single-class of WBCs (mAP = 0.892). This is attributed to the fact that WBCs are bigger objects and as such are easier examples to learn as compared to smaller objects (trophozoites only). The better performance of the multi-class task is attributed to having bigger objects complementing small objects (trophozoites). The multi-class mAP value of 0.6609 (@0.5 IOU) was considered to be good enough performance because of the presence of small objects(trophozoites). We notice that the recall value generated by SSD MobileNet is lower than that of Faster R-CNN to a great extent. This could be attributed to the fact that using more complex (two-stage) object detectors, such as Faster R-CNN, for difficult small images(parasites), provides better optimization than less complex (single-stage) detectors (SSD) [42]. We show that Faster R-CNN ResNet 101 model produces better detection accuracy for our proposed multi-class detection task for trophozoites (F1 score = 0.7897) and WBCs (F1 score = 0.8426)  
Figure 4: mAP graph and the corresponding training loss graph for Single Shot Multibox Detector (SSD) MobileNet. The model learns the data, while achieving a high accurac at about 80,000 iterations. Figure 3: mAP graph and the corresponding training loss graph for Faster R-CNN. The model learns the data while achieving a high accuracy at about 98,700 iterations. than its variant SSD MobileNet v2 model for detection of trophozoites (F1 score = 0.6038) and WBCs (F1 score = 0.7528). Furthermore, in Figure 6, Faster R-CNN model-generated parasite counts are higher than both SSD and manual-expert counts, which shows that Faster R-CNN models perform better at identification of parasites and WBCs in comparison to SSD and experts. The performance disparity with respect to the manual expert count could potentially signify the effect of false-positives through low quality annotations, thus a lower precision registered for parasites. We next analyze the magnitude of the disparity between Faster R-CNN and manual expert count. Results generated show that Faster R-CNN model-generated counts correlate well with manual-expert counts on a held-out dataset of 8 thick blood smear films with more than 100 images each (high Spearman's correlation coefficients for trophozoites (\(\rho\) = 0.998) and WBCs (\(\rho\) = 0.987)), as presented in Figure 7. In general, the precision, recall, and F1 score of Faster R-CNN multi-class detection task were comparably higher than SSD. Figure 5: Results for detection with Faster R-CNN (**b**) and SSD (**c**) for a sample image (**a**). The blue rectangles indicate WBCs, and the green rectangles indicate malaria trophozoites. The yellow circle indicates a False Negative. Figure 6: Manual experts vs Faster R-CNN model vs SSD model counts per blood film. Figure 7: Correlation between model counts and manual expert counts per film.