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Imagelytics: A Deep Learning-Based Image Classification Tool to Support Bioassessment

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Distributed Computing and Artificial Intelligence, Special Sessions II - Intelligent Systems Applications, 20th International Conference (DCAI 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 742))

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Abstract

Bioassessment is the process of using living organisms to assess the ecological health of a particular ecosystem. It typically relies on identifying specific organisms that are sensitive to changes in environmental conditions. Benthic macroinvertebrates are mostly used as bioindicators of the ecological status of freshwaters. However, a time-consuming process of species identification that requires high expertise represents one of the key obstacles to reliable bioassessment of aquatic ecosystems. Partial automation of this process using deep learning-based image classification is the goal of an ongoing project AIAQUAMI we are participating in. One of the project deliverables is a standalone desktop application for image classification with visualization and reporting that we developed and open-sourced as Imagelytics. The application relies on a convolutional neural network (CNN) to classify images and the Grad-CAM algorithm to produce heatmaps of the image areas that mostly influenced the network decision. Along with the application code, we also open-sourced scripts that can be used to train CNN on an arbitrary dataset and produce required metadata, so it can be used with Imagelytics. In this paper, we presented technical details about the application and training method that will enable its general use for image classification tasks. As a part of the evaluation, we will show a use case related to species identification of non-biting midges (Diptera: Chironomidae).

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Acknowledgment

This research was supported by the Science Fund of the Republic of Serbia, #7751676, Application of deep learning in bioassessment of aquatic ecosystems: toward the construction of automatic identifier of aquatic macroinvertebrates - AIAQUAMI.

Author information

Authors and Affiliations

  1. Faculty of Electronic Engineering, University of Niš, Aleksandra Medvedeva 14, 18000, Niš, Serbia

    Aleksandar Milosavljević & Bratislav Predić

  2. Faculty of Sciences and Mathematics, Department of Biology and Ecology, University of Niš, Višegradska 33, 18000, Niš, Serbia

    Djuradj Milošević

Authors
  1. Aleksandar Milosavljević
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  2. Bratislav Predić
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  3. Djuradj Milošević
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Corresponding author

Correspondence to Aleksandar Milosavljević .

Editor information

Editors and Affiliations

  1. University of A Coruña, A Coruña, Spain

    Esteban Jove

  2. University of A Coruña, A Coruña, Spain

    Francisco Zayas-Gato

  3. University of A Coruña, A Coruña, Spain

    Álvaro Michelena

  4. University of A Coruña, A Coruña, Spain

    José Luis Calvo-Rolle

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Milosavljević, A., Predić, B., Milošević, D. (2023). Imagelytics: A Deep Learning-Based Image Classification Tool to Support Bioassessment. In: Jove, E., Zayas-Gato, F., Michelena, Á., Calvo-Rolle, J.L. (eds) Distributed Computing and Artificial Intelligence, Special Sessions II - Intelligent Systems Applications, 20th International Conference. DCAI 2023. Lecture Notes in Networks and Systems, vol 742. Springer, Cham. https://doi.org/10.1007/978-3-031-38616-9_5

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