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Using convolutional neural networks for tick image recognition – a preliminary exploration

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Abstract

Smartphone cameras and digital devices are increasingly used in the capture of tick images by the public as citizen scientists, and rapid advances in deep learning and computer vision has enabled brand new image recognition models to be trained. However, there is currently no web-based or mobile application that supports automated classification of tick images. The purpose of this study was to compare the accuracy of a deep learning model pre-trained with millions of annotated images in Imagenet, against a shallow custom-build convolutional neural network (CNN) model for the classification of common hard ticks present in anthropic areas from northeastern USA. We created a dataset of approximately 2000 images of four tick species (Ixodes scapularis, Dermacentor variabilis, Amblyomma americanum and Haemaphysalis sp.), two sexes (male, female) and two life stages (adult, nymph). We used these tick images to train two separate CNN models – ResNet-50 and a simple shallow custom-built. We evaluated our models’ performance on an independent subset of tick images not seen during training. Compared to the ResNet-50 model, the small shallow custom-built model had higher training (99.7%) and validation (99.1%) accuracies. When tested with new tick image data, the shallow custom-built model yielded higher mean prediction accuracy (80%), greater confidence of true detection (88.7%) and lower mean response time (3.64 s). These results demonstrate that, with limited data size for model training, a simple shallow custom-built CNN model has great prospects for use in the classification of common hard ticks present in anthropic areas from northeastern USA.

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Authors and Affiliations

  1. Department of Health & Wellness Design, School of Public Health, Indiana University, 1025 E. 7th Street, Bloomington, IN, 47405, USA

    Oghenekaro Omodior

  2. Department of Kinesiology, School of Public Health, Indiana University Bloomington, Bloomington, IN, USA

    Mohammad R. Saeedpour-Parizi

  3. Department of Computer Science, School of Informatics, Computer Science and Engineering, Indiana University Bloomington, Bloomington, IN, USA

    Md. Khaledur Rahman

  4. Department of Intelligent Systems Engineering, School of Informatics Computer Science and Engineering, Indiana University Bloomington, Bloomington, IN, USA

    Ariful Azad

  5. Department of Ecology & Evolutionary Biology, Tulane University, New Orleans, LA, USA

    Keith Clay

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  1. Oghenekaro Omodior
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Correspondence to Oghenekaro Omodior.

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The authors declare no real or perceived conflict of interest. No human subjects were used in this study. This project was supported by the Environmental Resilience Institute (ERI), funded by Indiana University’s Prepared for Environmental Change (PfEC) Grand Challenge initiative.

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Omodior, O., Saeedpour-Parizi, M.R., Rahman, M.K. et al. Using convolutional neural networks for tick image recognition – a preliminary exploration. Exp Appl Acarol 84, 607–622 (2021). https://doi.org/10.1007/s10493-021-00639-x

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