# TickPhone App: A Smartphone Application for Rapid Tick Identification Using Deep Learning
###### Abstract
Only 10.5% of the participants could identify the female black-legged tick, which is the carrier of Lyme disease, before specific training with a manual, and the percentage of identification only increased to 48.7% after the training [16]. In addition, there are some institutes and companies that offer fee-based tick identification and testing services. However, it usually takes several days to receive the test results because tick samples need to be sent to their laboratories. Therefore, there is an unmet need to develop a simple, accurate, and cost-effective approach that can be used to rapidly identify tick species by untrained individuals in the field or at the point of care. Rapid advances in consumer electronics and mobile communication devices have revolutionized our daily lives. Ubiquitous smartphones, with their advanced computing capability and built-in functional modules (e.g., Global Positioning System, smartphone camera), provide unprecedented opportunities in remote disease diagnostics, mobile health, and public health surveillance, particularly in resource-limited settings where well-trained personnel and laboratory facilities are limited [19; 20]. In recent years, deep learning has emerged as a powerful technology in various research fields, such as natural language processing, speech recognition, and especially image recognition [21; 22; 23; 24; 25]. The application of image recognition technology can be widely used in daily life and various research areas. For instance, mobile bank applications use handwritten character recognition systems to identify bank checks. In the agriculture field, [PERSON] et al. used the vein morphological pattern to develop a deep learning model to identify plants [26], and [PERSON] et al. used the EfficientNet deep learning model to classify plant leaf diseases [27]. In the medical field, [PERSON] et al. developed a deep learning method to detect brain tumors in MRI [28], and [PERSON] et al. developed a machine learning-based mobile application for skin cancer detection [29]. Fingerprint recognition and face recognition systems developed based on deep learning are embedded in smartphones to authenticate the user's identity, thereby enhancing convenience [30]. The integration of deep-learning based image-recognition models with ubiquitous smartphones will lead to the development of a mobile diagnostic/detection platform, which could create a paradigm shift towards affordable and personalized health monitoring in the field or at home. Here, for the first time, we validated and optimized a deep learning model, and developed a smartphone-based deep learning algorithm (termed \"TickPhone app\") for rapid and accurate tick identification. The TickPhone app can be used by minimally trained individuals by simply taking a photograph of a tick, without the need for entomology expertise and expensive equipment. To improve the performance of the app, we enlarged the original dataset by data augmentation and optimized the deep learning model by several major parameters, such as the normal size of images, deep learning network architectures, image styles, and training-testing dataset distributions. The app was evaluated and validated for rapid and accurate tick identification by testing tick samples.