{
  "abstract": "This study is a multi-stage method for accurately segmenting and classifying breast tissue in thermographic images, with a specific focus on lateral views captured at multiple angles (30{degrees}, 45{degrees}, and 90{degrees}). Our algorithm is a hybrid boundary detection framework that combines scanning, tracking, and a deep reinforcement learning (DRL) algorithm to delineate the external breast tissue boundary precisely. The DRL agent corrects inaccurately detected landmark points and reduces the average localization error to 17.4 pixels. A Statistical Shape Model (SSM) captures shape variability and refines landmark alignment. A geometric arc-based method estimates the posterior breast boundary, which facilitates robust breast tissue isolation across various lateral view angles. After that, we pull texture features using GLCM (Grey Level Co-occurrence Matrix) and feed them through an SVM classifier. The proposed pipeline boosted diagnostic performance - the SVM hit 74.1% accuracy on our segmented images. It is about 15% better than raw originals and 21% above cropped images. The results prove the effectiveness of our multi-angle lateral view analysis combined with reinforcement learning and statistical shape modelling in enhancing breast cancer detection from thermographic imaging.",
  "abstract_shown": true,
  "affiliations": [
    "Shahed University"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "name": "F. Karimi"
    },
    {
      "affs": [
        0
      ],
      "name": "A. H. Foruzan"
    },
    {
      "name": "Y.-W. Chen"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/5eb41aqwh90fm.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.20.760338",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.10.20.760338v1.full",
  "id": "5eb41aqwh90fm",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.10.20.760338v1",
  "language": null,
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "bioengineering"
    ],
    [
      "openalex",
      "2741|Infrared Thermography in Medicine"
    ]
  ],
  "openalex": "W7223336178",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.10.20.760338v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-20",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/5eb41aqwh90fm.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:29:20Z",
      "native_id": "10.64898/2026.10.20.760338",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.10.20.760338v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioengineering",
      "subfield": "systems-synthetic"
    }
  ],
  "title": "Multi-Angle Lateral-View Breast Tissue Classification in Thermographic Images Using Deep Reinforcement Learning and Statistical Shape Modelling",
  "title_text": "Multi-Angle Lateral-View Breast Tissue Classification in Thermographic Images Using Deep Reinforcement Learning and Statistical Shape Modelling",
  "type": "preprint",
  "updated_at": "2026-10-30T12:29:20Z",
  "url": "https://research.draconitic.com/paper/5eb41aqwh90fm",
  "venue": "bioRxiv",
  "versions": []
}