## 1 Introduction
The authors of [15] automate the diagnostic process with the use of an intelligent system that would recognize malaria parasites, and aid efforts for the total elimination of malaria using microscopy and a specifically tailored CNN that outperforms VGGNet [15]. The proposed algorithm is based on the YOLO (You Only Look Once) method [16], a two-stage detector for objects in images. The initial phase of the method involves producing a broad array of region proposals for each image using either a heuristic algorithm or CNN [17], followed by the classification and regression of these identified candidate regions. In the context of object detection using computer vision, the main approaches followed in the literature are one and two-stage detectors [18]. YOLO is the most known example of a one-stage detector, which obtains a prediction of the existence of an object in a bounding box and classifies it into one of the different classes in one only inspection of the original image. Other well-known one-stage strategies are RetinaNet [19] and the Single Shot Multibox Detector (SSD) [20]. All one-stage strategies have in common that they divide the input image into cells to obtain a prediction of the subarea in the grid where the potential objects are present in their different classes, just using each cell at a time. They are particularly effective in real-time applications and commonly selected in low-computational scenarios because they are focused on the time needed to detect the objects. As for two-stage detectors, although typically slower, they usually obtain more accurate object detections [21]. The main difference with one-stage mechanisms is how two-stage mechanisms propose the regions and how they classify the objects in their classes. In this case, the process is divided into two steps: first, the detector selects certain areas of the input image that have more likelihood of having the presence of the pursued object, using methodologies like RPN (region proposal networks) or selective search. Once the potential areas are identified, they are refined and classified to conclude whether there is an existence of the object and where. One-stage methods use the whole input image to obtain a prediction of the bounding boxes; two-stage mechanisms, on the other hand, perform a refinement of the selected areas of the input image (proposed in the first step) and accomplish the obtention of the bounding boxes and the classification based on the features found in the first stage. Two-stage methods do not focus on speed but accuracy is a common choice when computation resources are not an issue and the objects in the image present difficulties like overlapping or a high density [22]. In the context of this research, performing detection in a single step provided faster detection and allowed a simpler yet effective approach; the speed advantage was key to enabling real-time detection not only in still images but also in videos and life detections on the microscope. Most mollusk identification processes rely on human visual ability and are an extensive and laborious task. This presents major limitations in terms of efficiency and scalability. In [23], the different techniques used in the scientific literature are shown. The morphological mechanisms using microscopic examinations are the primitive yet still more used method, despite the subjective nature of its philosophy (comparing images with voucher collections and preserved specimens [23]. Due to being subjective, it is a method clearly prone to human error [23]. The immunological techniques, on the other hand, rely on unique'signature' molecules that, once injected into a vertebrate host, will trigger an immune response that generates identifiable antibodies [23]; in this vein, polyclonal antibodies excel because of the higher affinity and wider reactivity, but it is an expensive method that only provides a markup for the individuals in the sample (keeping the classification according to size/age and account to the human operator).