{
  "abstract": "Colorectal cancer is the third most common malignancy worldwide. Manual screening requires expertise and resources. However, advancements in AI (artificial intelligence) have reduced the computation burden and time. Machine and deep learning have recently been used to diagnose colorectal lesions. The requirement of handcrafted features makes machine learning models expertise-dependent. At the same time, classical CNN (convolutional neural network) miss the global attention of the features. This work presents CDCTNet (colorectal diagnosis convolution transformer network), a hierarchical model for colorectal disease detection. Our model utilized two convolution blocks for the local high-dimensional spatial features from the lesion. In addition, the ViT encoder is used in parallel with the CNN block to provide a global correlation of the feature map. Furthermore, we designed an IEM block for the interaction of the features between the convolution block and ViT encoder to improve the attention on the features. The CDCTNet is evaluated on Kather and Kvasir datasets and obtained a precision and Kappa score of 96.60% and 95.02%, respectively. At the same time, CDCTNet has recall and F1 scores of 98.08% and 97.94%.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Computer Engineering & Applications, G.L.A. University, Mathura, Uttar Pradesh, India",
    "Centre for Research Impact & Outcome, Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, India",
    "Department of Electronics and Communication Engineering, Kuwait College of Science and Technology (KCST), Kuwait City, Kuwait"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Dhirendra Prasad Yadav"
    },
    {
      "affs": [
        1
      ],
      "name": "Bhisham Sharma"
    },
    {
      "affs": [
        2
      ],
      "name": "Julian L. Webber"
    },
    {
      "name": "Abolfazl Mehbodniya"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/86xq1r62wv5az.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pone.0357664",
  "fulltext_url": null,
  "id": "86xq1r62wv5az",
  "issns": [
    "1932-6203"
  ],
  "landing_url": "https://doi.org/10.1371/journal.pone.0357664",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1702|AI in cancer detection"
    ],
    [
      "plos",
      "/Biology and life sciences/Anatomy/Histology"
    ],
    [
      "plos",
      "/Computer and information sciences/Artificial intelligence/Machine learning"
    ],
    [
      "plos",
      "/Computer and information sciences/Artificial intelligence/Machine learning/Deep learning"
    ],
    [
      "plos",
      "/Medicine and health sciences/Anatomy/Histology"
    ],
    [
      "plos",
      "/Medicine and health sciences/Diagnostic medicine/Cancer detection and diagnosis"
    ],
    [
      "plos",
      "/Medicine and health sciences/Gastroenterology and hepatology/Gastrointestinal cancers"
    ],
    [
      "plos",
      "/Medicine and health sciences/Oncology/Cancer detection and diagnosis"
    ],
    [
      "plos",
      "/Medicine and health sciences/Oncology/Cancer treatment"
    ],
    [
      "plos",
      "/Medicine and health sciences/Oncology/Cancers and neoplasms/Colorectal cancer"
    ],
    [
      "plos",
      "/Research and analysis methods/Mathematical and statistical techniques/Mathematical functions/Convolution"
    ]
  ],
  "openalex": "W7207757265",
  "pdf_url": "https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0357664&type=printable",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-03",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/86xq1r62wv5az.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:56Z",
      "native_id": "10.1371/journal.pone.0357664",
      "source": "plos",
      "url": "https://doi.org/10.1371/journal.pone.0357664"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "plos:oncology",
      "subfield": "oncology"
    },
    {
      "field": "medicine",
      "origin": "plos:anatomy",
      "subfield": "physiology"
    }
  ],
  "title": "Colorectal Lesion diagnosis using transformer and deep learning with multiscale feature interface",
  "title_text": "Colorectal Lesion diagnosis using transformer and deep learning with multiscale feature interface",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:56Z",
  "url": "https://research.draconitic.com/paper/86xq1r62wv5az",
  "venue": "PLOS One",
  "versions": []
}