Digital health · 2026
Deep ensemble of multi-head attention CNNs for histopathological image-based of lung and colon cancer diagnosis.
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Rahman A K Z Rasel, Swapno S M Masfequier Rahman, Raha Avi Deb, Biswas Sujit, Khan Shakil, Khushbu Katura Gania, Reza Ahmed Wasif, Bairagi Anupam Kumar, Aloteibi Saad, Moni Mohammad Ali
Аннотация
OBJECTIVES: Classifying lung and colon cancer from histopathological images remains a significant challenge due to the high degree of intra-class feature similarity and complex tissue morphology, particularly in lung cancer cases. While convolutional neural networks (CNNs) have demonstrated strong spatial feature extraction capabilities, they cannot inherently model long-range dependencies and global contextual relationships. Although attention-based methods partially address these limitations, they often suffer from overfitting, limited generalization across heterogeneous datasets, and insufficient interpretability for clinical adoption. To address these challenges, this study presents a Multi-Head Attention-Based Convolutional Neural Network (MHAB-CNN) ensemble framework that captures localized and global feature interactions critical for robust cancer classification.
METHODS: A k-fold cross-validation strategy is adopted to train multiple MHAB-CNN models, from which the empirically top-performing ones are selected and aggregated to form a compact ensemble. This approach improves robustness, reduces overfitting, and ensures computational efficiency. Grad-CAM-based visualizations interpret the discriminative regions influencing the model's predictions.
RESULTS: Experimental evaluation on the LC25000 dataset demonstrates that the proposed framework achieves an average validation accuracy of 99.84% across folds. Furthermore, the E3 ensemble configuration, comprising models M1, M6, and M9, achieves the highest classification score on the held-out test set.
CONCLUSION: The proposed MHAB-CNN ensemble framework effectively captures localized and global feature interactions critical for robust lung and colon cancer classification, while improving robustness, reducing overfitting, and enhancing interpretability for potential clinical adoption.
METHODS: A k-fold cross-validation strategy is adopted to train multiple MHAB-CNN models, from which the empirically top-performing ones are selected and aggregated to form a compact ensemble. This approach improves robustness, reduces overfitting, and ensures computational efficiency. Grad-CAM-based visualizations interpret the discriminative regions influencing the model's predictions.
RESULTS: Experimental evaluation on the LC25000 dataset demonstrates that the proposed framework achieves an average validation accuracy of 99.84% across folds. Furthermore, the E3 ensemble configuration, comprising models M1, M6, and M9, achieves the highest classification score on the held-out test set.
CONCLUSION: The proposed MHAB-CNN ensemble framework effectively captures localized and global feature interactions critical for robust lung and colon cancer classification, while improving robustness, reducing overfitting, and enhancing interpretability for potential clinical adoption.