ArticleJournal of imaging2025
Transfer Learning Fusion Approaches for Colorectal Cancer Histopathological Image Analysis.
Article in Journal of imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
1 citing paper in PubMed.
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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 (
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Registered trials
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