A new era in identification of tick genera; artificial intelligence for precision and speed.

Background: The occurrence of pandemics in the last 20 years highlighted the unpreparedness of healthcare systems. There is a worldwide increased trend in the vector borne diseases. Ticks are one of the most common organisms that play a vital role in global ecosystem as well as being vectors of diseases affecting human and livestock. They are able to carry infectious agents that might cause illnesses including paralysis and to some certain extend death. Therefore, it is crucial to identify different genera of ticks to track infectious agents. Conventionally, tick classification is done by acarologists who are experts in the field. For this reason, the identification process is carried out in a difficult and time-consuming manner. Method: The aim of the study was to develop a web-based application by using artificial intelligence-based algorithms to easily identify Hyalomma and Rhipicephalus ticks, which are the most abundant genera in Northern Cyprus, with high sensitivity and accuracy. The experimental procedure is structured based on five phases. Phase 1 revolves around data collection in which pictures of 35 identified ticks are taken by experienced acarologists and the curation of non-tick images (spiders, beetles, mites, mosquitos and scorpions). Phase 2 revolves around pre-processing steps and data split. Phase 3 involves training and testing custom Convolutional Neural Network (CNN), Visual Geometry Group 16 (VGG16), Residual Network 50 (ResNet-50) using 6,972 images (3,486 images for each class) for discrimination between ticks and non-ticks and 9,556 images (4,778 images for each class) for the discrimination between Hyalomma and Rhipicephalus. Phase 4 revolves around performance evaluation. Phase 5 is characterized by development of a web-based application (I-TickNet), created to enable a widespread use of the tick classifier. Results: The performance evaluation and comparison of the model performance has shown that ResNet50 achieved the best result for binary classification of tick and non-tick (experiment A) with accuracy of 100% and Area Under the Curve (AUC) score of 100%. Moreover, VGG16 achieved the best result for binary classification of ticks (experiment B) with an accuracy of 96.97% and AUC score of 99.55% respectively. All the three models were employed for the development of artificial intelligence/Internet of Things (AI/IoT) framework known as I-TickNet for real-time and on-spot classification of tick images. In conclusion, this study provided a web-based application that can identify two distinct tick genera with high accuracy and sensitivity. The application developed enabled a user-friendly interface to identify genera without requiring any expertise.