## 1 Real-Time Protozoa Detection from Microscopic Imaging Using YOLOv4 Algorithm
\begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline
**Reference** & **Type of Organism** & **Database** & **Data Source** & **Number of Class** & **Pre-Processing Classifier** & **Segmentation/ Classifier** & **Performance Evaluation** & **Results (\%)** \\ \hline
[7] & Protozoa in wastewater & 1920 images & Original & 4 & Segmentation, decoding and resizing & Traditional NN, 6-layer CNN & Average accuracy & NN: 95.11, CNN: 99.59 \\ \hline
[19] & Malaria parasite & 27 images with 1288 \(\times\) 966 pixels & Original & 2 (malaria or not) & Grey level with histogram, equalization, median filter & Pretitur Edge Detector, ANN & Number of detections & Not specified detections & \\ \hline
[20] & Malaria parasite & 50 images with 1280 \(\times\) 960 pixels & Original & - & Noise filtering (downs filtering), contrast & Active Contour Without Edge, & Confusion matrix & Accuracy: 97.57 \\ \hline
[22] & Cholera pathogen, malaria & 372 slides & Original & 11 & Augmentation (random 250-by 250-pixel crops) & & Hierarchical & Binary accuracy, precision, and recall & Binary accuracy: 98, precision: 93, recall: 95 \\ \hline
[24] & Protozoa & 69 images with various pixels & Original & 8 & Data augmentation & Faster R-CNN, SSD, RetinaNet & Malaria detection in full & Faster R-CNN & max: 94.48, SSD mAP: 83.97, RetinaNet nAP: 74.46 \\ \hline
[25] & Malaria with 750 pixels & Original & 2 (malaria or 750 pixels) & Data (horizontal flip augmentation) & Faster R-CNN, SSD, RetinaNet & Malaria detection in full & Faster R-CNN & max: 94.48, SSD mAP: 83.97, RetinaNet nAP: 77.46 \\ \hline
[26] & Intestinal parasite & LAR-2: 1852, ECG-9: 12.691, PRO-7: 37.375 & LAR-2: 2, ECG-9: 77 & LAR-2: 2, ECG-9: 7.7 7 & IFT-based image processing & Hybrid System DS(1p-SVM) + operations & Average mean (PKO-7): 91.01 \\ \hline
[27] & Leishmaniasis & 45 images with 1500 \(\times\) 1300 pixels & Original & 7 & Data augmentation & U-Net score, precision, recall, and f1-score & Average (Dice score: 80.01, precision: 79.15, recall: 75.53, f1-score: 75.85) \\ \hline
[28] & Bacillus anthracis bacteria & 956 images with 512 \(\times\) 512 pixels & Original & 1 & Feature normalization & UnNet++ detection in full & Recall: 98, accuracy: 97 \\ \hline
[29] & Malaria with 1388 \(\times\) 1040 pixels & Original & 2 (malaria or not) & Noise filtering & CNN1 (40 \(\times\) 40), CNN2 (100 \(\times\) 100), fusion process & Full image, accuracy, sensitivity, specificity, f1-score & Accuracy (CNN2): 88.91 \\ \hline
[30] & Malaria with 750 \(\times\) 750 pixels & Public malaria dataset & 2 (malaria or not) & Not applied modified, YOLOV4 & Malaria detection in full & YOLOV4 & Malaria mAP: 96.32, YOLOV3 & YOLOV3 MODI \\ \hline
[31] & Malaria with 3264 \(\times\) 2448 pixels & Original & 2 (malaria or not) & Data augmentation & Faster R-CNN, SSD MobileNet & Malaria detection in full & Faster R-CNN & max: 66.09, SSD MobileNet \\ \hline \hline \end{tabular}
\end{table} 
Table 1: A summary of studies on the identification of bacteria on images. Utilizing deep learning for microscopic parasite diagnosis has the potential to significantly enhance efficiency for medical professionals, leading to a decrease in instances of misdiagnosis, overlooked diagnoses, and inappropriate medication usage. Despite the advancements achieved in the application of deep learning to diagnose protozoan parasites, several problems still persist. The main problem is the limited number of labeled datasets publicly available. To overcome this problem, some methods are used to extend datasets [32]. But, in medical studies, real data should be used instead of artificial data. However, different image reproduction methods are used in cases where there are not enough images. The datasets on the internet resources were not sufficient for our study and were not sufficient in terms of image quality. Therefore, unlike other methods, in this study, completely real data were used without needing image reproduction methods.