{
  "abstract": "Bioassays offer sensitive and cost-effective methods for detecting heavy metal contamination. In this study, we further integrate convolutional neural networks (CNNs) into bioassays, enabling high-throughput analysis. Specifically, we detect copper or zinc contamination through cell images of Egeria densa leaves. Four CNN models (InceptionV3, EfficientNetB1, ResNet152, and DenseNet121) achieved an accuracy of 99% in distinguishing among 0 ppm (control), 0.1 ppm copper, and 0.1 ppm zinc contamination. Other metrics, including precision, recall, and F1-score, all reached 0.99. Furthermore, when classifying three concentrations (0, 0.1, and 0.5 ppm) of copper or zinc contamination, ResNet152 achieved an accuracy of 98.4% for copper and 98.3% for zinc, and all models achieved an accuracy of over 93%, demonstrating their ability to distinguish between contamination severities. The results suggest that the combination of CNN with bioassays has the potential to monitor heavy metal contamination in the environment.",
  "abstract_shown": true,
  "affiliations": [
    "National Taipei University of Technology , , No.1, Sec. 3, Zhongxiao E. Road , ,"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Ching-Jui Tseng"
    },
    {
      "affs": [
        0
      ],
      "name": "Chen-Chen Wang"
    },
    {
      "affs": [
        0
      ],
      "name": "Chung-Heng Liu"
    },
    {
      "affs": [
        0
      ],
      "name": "You-Chen Gou"
    },
    {
      "affs": [
        0
      ],
      "name": "Che-Chi Shu",
      "orcid": "0000-0002-6930-1050"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1gv9xwdxhx13n.bib",
  "cited_by": 0,
  "doi": "10.1021/acsomega.6c02216",
  "fulltext_url": null,
  "id": "1gv9xwdxhx13n",
  "issns": [
    "2470-1343"
  ],
  "landing_url": "https://pubs.acs.org/acsodf/article/doi/10.1021/acsomega.6c02216/5466440/Using-Convolutional-Neural-Networks-to-Detect",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [],
  "openalex": null,
  "pdf_url": "https://pubs.acs.org/doi/pdf/10.1021/acsomega.6c02216",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-09",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/1gv9xwdxhx13n.ris",
  "sources": [
    {
      "first_seen": "2026-11-09T12:22:10Z",
      "native_id": "10.1021/acsomega.6c02216",
      "source": "crossref",
      "url": "https://pubs.acs.org/acsodf/article/doi/10.1021/acsomega.6c02216/5466440/Using-Convolutional-Neural-Networks-to-Detect"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "issn:2470-1343",
      "subfield": "general"
    }
  ],
  "title": "Using Convolutional Neural Networks to Detect Copper and Zinc Contamination through Cell Images of Egeria densa Leaf",
  "title_text": "Using Convolutional Neural Networks to Detect Copper and Zinc Contamination through Cell Images of Egeria densa Leaf",
  "type": "peer-reviewed",
  "updated_at": "2026-11-10T00:21:21Z",
  "url": "https://research.draconitic.com/paper/1gv9xwdxhx13n",
  "venue": "ACS Omega",
  "versions": []
}