City Research Online

Abstract

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 longrange 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 (MHABCNN) 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 topperforming 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.

Publication Type:	Article
Additional Information:	© The Authors. Published by Sage. This is an open-access article distributed under the terms of Creative Commons: Attribution International Public License 4.0 (http://creativecommons.org/licenses/by/4.0/).
Publisher Keywords:	ensemble, CNN, multihead attention, lung cancer, colon cancer, explainable AI
Subjects:	H Social Sciences > HN Social history and conditions. Social problems. Social reform Q Science > QA Mathematics > QA75 Electronic computers. Computer science R Medicine > RC Internal medicine R Medicine > RC Internal medicine > RC0254 Neoplasms. Tumors. Oncology (including Cancer)
Departments:	School of Science & Technology School of Science & Technology > Department of Computer Science
SWORD Depositor:	Symplectic Administrator

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