{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T02:06:32Z","timestamp":1777860392452,"version":"3.51.4"},"reference-count":44,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2024,9,3]],"date-time":"2024-09-03T00:00:00Z","timestamp":1725321600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,7,25]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>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 widely used for examining the ecological status of freshwaters. However, a time-consuming process of species identification that requires high expertise represents one of the key obstacles to more precise 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 goals is to develop software support for image classification with visualization and reporting. For that purpose, we developed desktop and web applications that we open-sourced as Imagelytics Suite. Both desktop and web applications rely 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 source code of the applications, 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 applications. In this article, we presented technical details regarding the design of the applications and the training method that will enable their 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).<\/jats:p>","DOI":"10.1093\/jigpal\/jzae103","type":"journal-article","created":{"date-parts":[[2024,8,14]],"date-time":"2024-08-14T18:53:20Z","timestamp":1723661600000},"source":"Crossref","is-referenced-by-count":0,"title":["Imagelytics suite: deep learning-powered image classification for bioassessment in desktop and web environments"],"prefix":"10.1093","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3057-7727","authenticated-orcid":false,"given":"Aleksandar","family":"Milosavljevi\u0107","sequence":"first","affiliation":[{"name":"University of Ni\u0161 Faculty of Electronic Engineering, , Aleksandra Medvedeva 14, 18000 Ni\u0161, , 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