## 2 Related Work  
Cell detection, segmentation, and counting are computer vision tasks well addressed in the literature. While all these tasks allow us to obtain the number of cells, the most suitable approach for a specific problem depends on the target goal. In particular, the detection task provides the localization in the form of a bounding box and the respective class, while density estimation only gives the final number of objects. Alternatively, segmentation approaches allow for obtaining the mask and respective class of the detected objects. For such tasks, the state-of-the-art approaches proposed in the literature mostly rely on machine learning and deep learning methods. Works such as [7; 8; 9] propose approaches based on deep learning, such as U-Net and feature pyramid network (FPN) networks, to perform cell detection and segmentation. A single-shot detector (SSD) in pair with a convolutional neural network (CNN) to localize and count different blood cell types was addressed in [10]. Furthermore, microscopy cell counting based on density estimation employing fully convolutional regression networks was proposed in [11]. In all approaches mentioned above, the authors reported results with performances comparable with human specialists. Until [5], a less explored field in the literature was related to automated smear adequacy assessment, i.e., the development of computational methods to ensure that cervical samples are adequate for further analysis. In [12], the authors describe an AI-assistive diagnostic solution to improve cervical liquid-based thin-layer cell-smear diagnosis according to clinical TBS criteria. The developed system consists of five AI models which are employed to detect and classify the lesions. A You-Only-Look-Once (YOLO)v3 model was used for target detection, Xception, and patch-based models to cope with the high number of false positives detected and U-net for nucleus segmentation. The final classification was performed via two ensembled XGboost models, being developed and evaluated using a dataset of 80,000 LBC samples collected from five medical institutes. Regarding quality assessment, the procedure is applied to the entire sample and comprises focus, contrast, and quantitative cell evaluations. For this task, a simpler approach was followed: the Otsu thresholding method is initially used to separate the cells from the background, and the cell-to-overall-area ratio is then used to obtain a rough number of cells in the sample. The average accuracy of 99.11% was reported on the task of sample classification as satisfactory or unsatisfactory. However, it must be noted that TBS guidelines were not strictly followed since this method estimates the total number of cells in the sample, including other types of nuclei, aside from squamous nuclei, that should not be considered to assess sample cellularity. Still on the topic of using deep-learning-based approaches to detect and classify cervical lesions, several recent works proved its feasibility to support cervical cancer screening, with proposed approaches that explored the usage of different deep convolutional neural networks ([13; 14]) and architectures, such as MobileNet [15; 16], EfficientNet [15; 17], as well as newly proposed networks, like the series-parallel fusion network (SPFNet) [18], Cervical Ensemble Network (CEENET) [19], or EfficientNet Fuzzy Extreme-Learning Machine (EN-FELM) [20]. Despite the promising results of these previous works, it should be noted that the vast majority do not take into account limitations like restricted computational resources to run the models. Additionally, most of them require the usage of high-end digital pathology whole-slide imaging (WSI) scanners, which are equipment not generally accessible in areas with limited access and restricted financial resources. As an alternative to regular microscopes and WSI scanners, the development of low-cost, portable microscopes that enable microscopy-based diagnosis has also emerged in the literature.