Expedited Colorectal Cancer Detection Through a Dexterous Hybrid CADx System With Enhanced Image Processing and Augmented Polyp Visualization
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 m...
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| Format: | Article |
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IEEE
2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10849566/ |
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| author | Akella Subrahmanya Narasimha Raju K. Venkatesh Ranjith Kumar Gatla Marwa M. Eid Aymen Flah Zdenek Slanina Ramy N. R. Ghaly |
| author_facet | Akella Subrahmanya Narasimha Raju K. Venkatesh Ranjith Kumar Gatla Marwa M. Eid Aymen Flah Zdenek Slanina Ramy N. R. Ghaly |
| author_sort | Akella Subrahmanya Narasimha Raju |
| collection | DOAJ |
| 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’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. |
| format | Article |
| id | doaj-art-6f1dbd861cff4fe29ea11b71edfec60e |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-6f1dbd861cff4fe29ea11b71edfec60e2025-01-31T00:00:50ZengIEEEIEEE Access2169-35362025-01-0113175241755310.1109/ACCESS.2025.353280710849566Expedited Colorectal Cancer Detection Through a Dexterous Hybrid CADx System With Enhanced Image Processing and Augmented Polyp VisualizationAkella Subrahmanya Narasimha Raju0https://orcid.org/0000-0002-3237-2859K. Venkatesh1https://orcid.org/0000-0002-2895-0839Ranjith Kumar Gatla2https://orcid.org/0000-0003-2154-3285Marwa M. Eid3Aymen Flah4https://orcid.org/0000-0002-3463-6096Zdenek Slanina5Ramy N. R. Ghaly6Department of Computer Science and Engineering (Data Science), Institute of Aeronautical Engineering, Hyderabad, Telangana, IndiaDepartment of Networking and Communications, School of Computing, SRM Institute of Science and Technology, Chennai, Tamil Nadu, IndiaDepartment of Computer Science and Engineering (Data Science), Institute of Aeronautical Engineering, Hyderabad, Telangana, IndiaDepartment of Physical Therapy, Collage of Applied Medical Science, Taif University, Taif, Saudi ArabiaNational Engineering School of Gabès, University of Gabès, Tunis, TunisiaENET Centre, Centre for Energy and Environmental Technologies (CEET), VSB—Technical University of Ostrava, Ostrava, Czech RepublicChitkara Centre for Research and Development, Chitkara University, Baddi, Himachal Pradesh, IndiaThe 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’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.https://ieeexplore.ieee.org/document/10849566/Enter colorectal cancer detectiondeep learning ensembleRDV-22BiLSTMvision transformersLIME visualization |
| spellingShingle | Akella Subrahmanya Narasimha Raju K. Venkatesh Ranjith Kumar Gatla Marwa M. Eid Aymen Flah Zdenek Slanina Ramy N. R. Ghaly Expedited Colorectal Cancer Detection Through a Dexterous Hybrid CADx System With Enhanced Image Processing and Augmented Polyp Visualization IEEE Access Enter colorectal cancer detection deep learning ensemble RDV-22 BiLSTM vision transformers LIME visualization |
| title | Expedited Colorectal Cancer Detection Through a Dexterous Hybrid CADx System With Enhanced Image Processing and Augmented Polyp Visualization |
| title_full | Expedited Colorectal Cancer Detection Through a Dexterous Hybrid CADx System With Enhanced Image Processing and Augmented Polyp Visualization |
| title_fullStr | Expedited Colorectal Cancer Detection Through a Dexterous Hybrid CADx System With Enhanced Image Processing and Augmented Polyp Visualization |
| title_full_unstemmed | Expedited Colorectal Cancer Detection Through a Dexterous Hybrid CADx System With Enhanced Image Processing and Augmented Polyp Visualization |
| title_short | Expedited Colorectal Cancer Detection Through a Dexterous Hybrid CADx System With Enhanced Image Processing and Augmented Polyp Visualization |
| title_sort | expedited colorectal cancer detection through a dexterous hybrid cadx system with enhanced image processing and augmented polyp visualization |
| topic | Enter colorectal cancer detection deep learning ensemble RDV-22 BiLSTM vision transformers LIME visualization |
| url | https://ieeexplore.ieee.org/document/10849566/ |
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