# A Smartphone-Based Cell Segmentation to Support Nasal Cytology
## 6 Conclusions  
The advancements in the nasal cytology field and the evolution of smartphone technology have allowed for the realization of this project. The aim of designing a system that would support the specialist during the observation phase of the slides has been reached through the development of  
Figure 12: Cell detection.  
this system, able to acquire an image from the digital microscope and to extract the cellular elements. The main advantages of this application is that the cell counting activity is faster than the manual process, together with its ease of use and the possibility of sharing images obtained from the observed fields. In fact, the cell images extracted can be sent directly to a specific server, which automatically classifies and counts them, such as the Rhino-Cyt system [23]. A possible use of this system could also be in combination with a microscope, which allows for the automatic sliding of the slide. The specialist could manage the sliding and acquire the photo, as necessary. We are now setting ourselves two main goals. The first is to pursue effective full classification on the board and the second is to integrate other diagnostic tools, such as the one just published in the literature, which aims to diagnose dyskinesia of the hair cells of the nasal mucosa [58].  
Conceptualization, G.D. and P.R.F.; data curation, L.S. and F.D.; formal analysis, F.D. and D.D.P.; methodology, G.D., F.D. and L.S.; project administration, G.D.; resources, G.D.; software, P.R.F. and D.D.P; validation, S.L.; writing--original draft, G.D. and D.D.P.; writing--review and editing, G.D., P.R.F., D.D.P. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The authors declare no conflict of interest. The authors declare no conflict of interest.  
As we have written in the introduction section, to the best of our knowledge, to date, there are no public or private laboratories that carry out the examination of the cell population of the nasal mucosa routinely, as instead it is done for hematological tests. The first studies about the automatic extraction and classification of the cells of the nasal mucosa were published by some of the authors of this paper. Now, specialists would prefer to carry out the entire evaluation of the cell population on a personal device, such as a smartphone, fully automatically, with the aim of increasing the screening and routine monitoring of nasal disease through cytology.  
The use of a smartphone-based system also guarantees the preservation of the privacy and security of patient information. On the other hand, it makes it possible to send patient data and images to the Electronic Medical Record [8] to follow-up with the patient or to obtain a \"second opinion\", an increasingly widespread practice. However, this further possibility is reserved for patients who request it and, for these, a security protocol should be used. When the classification is carried out completely on the smartphone, nothing must be transferred remotely; however, several problems have to be overcome first, among all the limitations of the computational capacity of mobile architectures.  
Our system is based on well-known algorithms in the literature--not state-of-the-art, but effective enough for our purpose. These are already sustainable from a computational point of view from medium-low end architectures, such as the Xiaomi, used for this experimentation. The use of traditional image processing techniques to preprocess the image is also battery-efficiently on a mobile phone.  
At this stage, the system designed and described in this paper is limited to the extraction of cells from the microscopic field. Once this is done, the specialist can decide to manually evaluate the segmented cells or to send them to the Rhino-cyt platform for a fast classification. So, the system is already very useful.