Expedited Colorectal Cancer Detection Through a Dexterous Hybrid CADx System With Enhanced Image Processing and Augmented Polyp Visualization

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F25%3A10257453" target="_blank" >RIV/61989100:27240/25:10257453 - isvavai.cz</a>

Alternative codes found

RIV/61989100:27730/25:10257453

Result on the web

<a href="https://ieeexplore.ieee.org/document/10849566" target="_blank" >https://ieeexplore.ieee.org/document/10849566</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ACCESS.2025.3532807" target="_blank" >10.1109/ACCESS.2025.3532807</a>

Result language

angličtina

Original language name

Expedited Colorectal Cancer Detection Through a Dexterous Hybrid CADx System With Enhanced Image Processing and Augmented Polyp Visualization

Original language description

The complexity and variability of medical imaging continue to impair the feasibility of early detection of colorectal cancer, despite its critical role in improving patient outcomes. This research presents a new multistage ensemble method that combines the strengths of three advanced deep learning models: vision transformers, RDV-22 (a mix of ResNet-50, DenseNet-201, and VGG-16), and Bidirectional Long Short-Term Memory (BiLSTM) networks. It improves the accuracy and robustness of Colorectal Cancer (CRC) detection. For suitable evaluation of our methodology, we adopted two benchmark datasets: CVC Clinic DB for binary classification and Kvasir dataset for multiclass classifications. To decrease the sizes of data significantly along with the generalizability of the model, we have used various data augmentation techniques, including magnification, inversion, and rotation. The best performing model was RDV-22+ BiLSTM + Vision Transformers, which recorded a 95.0% test accuracy and an area of 0.96 under the curve with the CVC Clinic DB dataset. The model showed a high performance in wide classes of samples such as esophagitis, polyps, and ulcerative colitis, with an accuracy rate of 92.5% and Area Under Curve (AUC) values 1.00 on the Kvasir dataset. We applied Local Interpretable Model-Agnostic Explanations (LIME) visualizations to both datasets, which proved to be an insightful identification of regions that the model relied on during the polyp detection process. Based on the LIME explanation, this analysis found that the visualizations generated by the Kvasir dataset were more informative and even clearer when complex cases, including ulcerative colitis and polyps are concerned. This indicates the model&apos;s ability to handle challenging medical images effectively. Because of the complexity and diversity of the datasets, these results constitute a significant improvement on the current methodology. There is expectation that the proposed ensemble method could significantly improve the dependability and precision of diagnosing colorectal cancer. More accurate diagnoses and faster diagnoses in clinical practice might improve patient outcomes and reduce death rates if this is successful. While the ensemble model presents highly improved performance, this computationally and dataset-specific performance requirement suggests further optimization and validation for larger clinical applicability.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

20200 - Electrical engineering, Electronic engineering, Information engineering

Project

<a href="/en/project/TN02000025" target="_blank" >TN02000025: National Centre for Energy II</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

IEEE Access

ISSN

2169-3536

e-ISSN

2169-3536

Volume of the periodical

13

Issue of the periodical within the volume

Volume: 13

Country of publishing house

US - UNITED STATES

Number of pages

30

Pages from-to

17524-17553

UT code for WoS article

001410367700005

EID of the result in the Scopus database

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