# TickPhone App: A Smartphone Application for Rapid Tick Identification Using Deep Learning
## 4 Conclusions  
In this study, we optimized and validated a deep learning algorithm, and developed a smartphone app, to rapidly identify tick species. The optimized deep learning model was developed using a convolutional neural network and converted to a TensorFlow Lite model for app development. The app allows an untrained individual to perform rapid and accurate tick identification by simply taking a photo of the tick using a smartphone, thus eliminating the need for entomological knowledge and expensive equipment. Our study showed that the optimized deep learning model can achieve a training accuracy of \(\sim\)90% and a validation accuracy of \(\sim\)85%. In the independent test with 31 tick specimens, our TickPhone app achieved an identification accuracy of 95.69%. In the receiver-operating characteristics curve analysis, we identified adult female deer ticks and adult male deer ticks with an AUC of 98% and 96%, respectively. Such a simple, rapid, and reliable approach has the potential to be used for tick identification in the field, at home, or at the point of need. In future work, deeper and new deep learning models (MobileNet, Efficient-B0, SqueezeNet) [39, 40, 41] will be used to improve the accuracy of the models, and backdoor or data poisoning methods will be adapted to improve the models' security [42].  
The following are available online at [[https://www.mdpi.com/article/10.3390/app11167355/s1](https://www.mdpi.com/article/10.3390/app11167355/s1)]([https://www.mdpi.com/article/10.3390/app11167355/s1](https://www.mdpi.com/article/10.3390/app11167355/s1)), Figure S1. The architecture of the conventional neutral network developed for tick identification; Figure S2. Interfaces of our TickPhone app. (A) Tick identification, and (B) spatiotemporal mapping of tick identification results; Figure S3. Three types of common ticks (e.g., deer tick, dog tick, and lone star tick) and their transmitting tick-borne diseases; Figure S4. Data augmentation from original pictures through different methods: (i) rotation with 30% range, (ii) height shift with 30% range, (iii) width shift with 30% range, (iv) shear with 30% range, (v) zoom with 30% range, and (vi) horizontal flip; Figure S5. Examples of the poor-quality tick photos caused by glare (A) or being out of focus (B); Table S1. Comparison of tick identification results obtained by our TickPhone app and provided by the Connecticut Veterinary Medical Diagnostic Laboratory.  
Figure 7: Receiver-operating characteristic curve analysis of identification of three common tick species, including female deer ticks, male deer ticks, female lone star ticks, male lone star ticks, female American dog ticks, and male American dog ticks.  
**Author Contributions:** [PERSON] and [PERSON] conceived the experiments, research goals, and smartphone application design. [PERSON], [PERSON] and [PERSON] performed the experiments. [PERSON] and [PERSON] developed the smartphone application. [PERSON], [PERSON] and [PERSON] collected and recognized field samples. [PERSON], [PERSON], [PERSON] and [PERSON] wrote the manuscript. All authors reviewed the manuscript. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by startup funds at the University of Connecticut Health Center.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data available from the authors upon a reasonable request.  
**Conflicts of Interest:** The authors declare no conflict of interest.