Transfer Learning Fusion Approaches for Colorectal Cancer Histopathological Image Analysis
It is well-known that accurate classification of histopathological images is essential for effective diagnosis of colorectal cancer. Our study presents three attention-based decision fusion models that combine pre-trained CNNs (Inception V3, Xception, and MobileNet) with a spatial attention mechanis...
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MDPI AG
2025-06-01
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| Series: | Journal of Imaging |
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| Online Access: | https://www.mdpi.com/2313-433X/11/7/210 |
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| author | Houda Saif ALGhafri Chia S. Lim |
| author_facet | Houda Saif ALGhafri Chia S. Lim |
| author_sort | Houda Saif ALGhafri |
| collection | DOAJ |
| description | It is well-known that accurate classification of histopathological images is essential for effective diagnosis of colorectal cancer. Our study presents three attention-based decision fusion models that combine pre-trained CNNs (Inception V3, Xception, and MobileNet) with a spatial attention mechanism to enhance feature extraction and focus on critical image regions. A key innovation is the attention-driven fusion strategy at the decision level, where model predictions are weighted by relevance and confidence to improve classification performance. The proposed models were tested on diverse datasets, including 17,531 colorectal cancer histopathological images collected from the Royal Hospital in the Sultanate of Oman and a publicly accessible repository, to assess their generalizability. The performance results achieved high accuracy (98–100%), strong MCC and Kappa scores, and low misclassification rates, highlighting the robustness of the proposed models. These models outperformed individual transfer learning approaches (<i>p</i> = 0.009), with performance differences attributed to the characteristics of the datasets. Gradient-weighted class activation highlighted key predictive regions, enhancing interpretability. Our findings suggest that the proposed models demonstrate the potential for accurately classifying CRC images, highlighting their value for research and future exploration in diagnostic support. |
| format | Article |
| id | doaj-art-3929e8c0099b49bc99ef40cd263bb5de |
| institution | Kabale University |
| issn | 2313-433X |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Imaging |
| spelling | doaj-art-3929e8c0099b49bc99ef40cd263bb5de2025-08-20T03:58:31ZengMDPI AGJournal of Imaging2313-433X2025-06-0111721010.3390/jimaging11070210Transfer Learning Fusion Approaches for Colorectal Cancer Histopathological Image AnalysisHouda Saif ALGhafri0Chia S. Lim1Department of Information Technology, College of Computing and Information Sciences, University of Technology and Applied Sciences, Muscat 133, OmanGraduate School of Technology, Asia Pacific University of Technology and Innovation, Kuala Lumpur 57000, MalaysiaIt is well-known that accurate classification of histopathological images is essential for effective diagnosis of colorectal cancer. Our study presents three attention-based decision fusion models that combine pre-trained CNNs (Inception V3, Xception, and MobileNet) with a spatial attention mechanism to enhance feature extraction and focus on critical image regions. A key innovation is the attention-driven fusion strategy at the decision level, where model predictions are weighted by relevance and confidence to improve classification performance. The proposed models were tested on diverse datasets, including 17,531 colorectal cancer histopathological images collected from the Royal Hospital in the Sultanate of Oman and a publicly accessible repository, to assess their generalizability. The performance results achieved high accuracy (98–100%), strong MCC and Kappa scores, and low misclassification rates, highlighting the robustness of the proposed models. These models outperformed individual transfer learning approaches (<i>p</i> = 0.009), with performance differences attributed to the characteristics of the datasets. Gradient-weighted class activation highlighted key predictive regions, enhancing interpretability. Our findings suggest that the proposed models demonstrate the potential for accurately classifying CRC images, highlighting their value for research and future exploration in diagnostic support.https://www.mdpi.com/2313-433X/11/7/210colorectal cancerdecision fusiontransfer learningspatial attention mechanismshistopathological images |
| spellingShingle | Houda Saif ALGhafri Chia S. Lim Transfer Learning Fusion Approaches for Colorectal Cancer Histopathological Image Analysis Journal of Imaging colorectal cancer decision fusion transfer learning spatial attention mechanisms histopathological images |
| title | Transfer Learning Fusion Approaches for Colorectal Cancer Histopathological Image Analysis |
| title_full | Transfer Learning Fusion Approaches for Colorectal Cancer Histopathological Image Analysis |
| title_fullStr | Transfer Learning Fusion Approaches for Colorectal Cancer Histopathological Image Analysis |
| title_full_unstemmed | Transfer Learning Fusion Approaches for Colorectal Cancer Histopathological Image Analysis |
| title_short | Transfer Learning Fusion Approaches for Colorectal Cancer Histopathological Image Analysis |
| title_sort | transfer learning fusion approaches for colorectal cancer histopathological image analysis |
| topic | colorectal cancer decision fusion transfer learning spatial attention mechanisms histopathological images |
| url | https://www.mdpi.com/2313-433X/11/7/210 |
| work_keys_str_mv | AT houdasaifalghafri transferlearningfusionapproachesforcolorectalcancerhistopathologicalimageanalysis AT chiaslim transferlearningfusionapproachesforcolorectalcancerhistopathologicalimageanalysis |