## 1 Real-Time Protozoa Detection from Microscopic Imaging Using YOLOv4 Algorithm
Microscopic images may have different characteristics according to the color and tone of the light, and the mobility of protozoa, the deformation of some organisms, and the pollutants in the water also affect the image state. The first of these is laboratory techniques. One of them is the physical and chemical spectrum method with PCR [11; 12; 13; 14]. In this technique, sample preparation, partitioning, PCR amplification, and detection steps were implemented. They analyzed some different implementations of dPCR for detection of protozoan objects in samples. In [15; 16], electrochemical detection was carried out. They build a point-of-care device containing a couple of electrochemical biosensors, media layers, and some other parts. It is difficult to build portable systems with high sensitivity and resilience, even when used with complicated matrices. In [17], microfluidic impedance cytometry method was applied. Another was DNA and RNA methods based on molecular biology [18]. The conventional approach for diagnosing parasites involves microscopic examination. Despite being labor-intensive and demanding experienced interpreters for optimal results, the proposed method remains extensively utilized for diagnosing protozoa, particularly in resource-limited settings. Nevertheless, numerous laboratories face a shortage of examiners proficient in consistently detecting the existence and classification of intestinal protozoa. This deficiency contributes to the incapacity to precisely recognize protozoa, distinguish pathogenic from non-pathogenic species, and discern artifacts during microscopic assessments, ultimately compromising the sensitivity and specificity of intestinal protozoa diagnosis. Secondly, image processing techniques were used to detect protozoan presence. In [19], their proposed method consisted of a pre-processing operation to activate images, dividing them into regions and applying edge detection for feature extraction, ANN classification of regions protozoa or non-protozoa, morphological operations for deciding the regions, and applying an active contour method to regions whose location could not be identified precisely. Thirty images were used. The suggested method's weakness is that it disregards the filamentous structure. In [20], the parasites were segmented with Active Contour Without Edge. They performed morphological operations for removing noise and thresholding to segment an image into two areas. Their dataset contains 50 images, and each file size is 140 \(\times\) 140 pixels. The accuracy of the work is 97.57% and the False Negative Rate is 12.04%. In [21], the authors proposed a method to identify malaria parasites from microscopic images. Their dataset contains 117 microscopic fields of 3136 \(\times\) 2352 pixels. In the beginning, they applied an image noise removal operation using Laplacian filter. The adaptive histogram thresholding technique was used to segment thin and thick smear images. As well, 8-connectivity was used to label the segmented images in HSV color space. According to the author, while the manual parasite count was 576, the parasite count by their proposed method was 627. Lastly, in the current studies, algorithms such as Classical CNN, Retina Net, R-CNN, Fast R-CNN, Faster R-CNN, You Look Only Once (YOLO), and single shot detector (SSD) are among the deep learning methods that stand out in the detection of objects in images with digital systems. In [22], a classical CNN model was used to detect epidemic pathogens vibrio cholera and plasmodium falciparum. The training dataset includes 400 images of both classes and 80 images for testing. A Tensorflow framework was conducted to apply the CNN. Their CNN model has 6 convolutional layers, ReLU and 2 \(\times\) 2 MaxPooling and follows by fully connected layer. Finally, the classification accuracy of the system is 94% and the validation accuracy is 97%. Another CNN method was used to detect Intestinal Protozoa by [PERSON] and others in [23].