Colorectal Lesion diagnosis using transformer and deep learning with multiscale feature interface

Dhirendra Prasad Yadav, Bhisham Sharma, Julian L Webber and Abolfazl Mehbodniya

PLOS ONE, 2026, vol. 21, issue 9, 1-20

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%.

Date: 2026
References: Add references at CitEc
Citations:

Downloads: (external link)
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357664 (text/html)
https://journals.plos.org/plosone/article/file?id= ... 57664&type=printable (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0357664

DOI: 10.1371/journal.pone.0357664

Access Statistics for this article

More articles in PLOS ONE from Public Library of Science
Bibliographic data for series maintained by plosone ().