Ensemble Learning Methods for CANCER
Ensemble Learning Methods for CANCER
Kusuma DevikaRani1, Sriramadasu Akshita2, MD.Mujeeb3, Mythri kulakarni4, U.Sanjeev Kumar5, Syeda Neha Shireen6
Student1-5, BTech (CSE) From Sphoorthy Engineering College, Hyderabad.
Assistant Professor, Dep of CSE, Sphoorthy Engineering College, Hyderabad
Abstract
The rapid advancement of Artificial Intelligence (AI) has significantly transformed modern healthcare, particularly in the domain of automated disease diagnosis. However, existing diagnostic systems often rely on unimodal data sources, leading to incomplete analysis and reduced predictive reliability. This study proposes a novel Automated Multimodal Ensemble Learning Framework for Cancer Diagnosis, which integrates heterogeneous data modalities, including medical imaging and clinical textual information, to enhance diagnostic accuracy and robustness. The framework employs deep Convolutional Neural Networks (CNNs) for hierarchical feature extraction from medical images and Transformer-based architectures for contextual understanding of clinical narratives. These modality-specific representations are subsequently integrated using hybrid fusion strategies, enabling the system to capture complementary information effectively. To further improve performance, ensemble learning techniques such as voting and stacking are incorporated, reducing variance and mitigating overfitting. The proposed system is evaluated using standard performance metrics, including Accuracy, Precision, Recall, F1-score, and AUC, demonstrating superior performance compared to individual models. Experimental results indicate that the multimodal ensemble approach significantly enhances classification accuracy, stability, and predictive confidence. The system also provides probabilistic outputs and explainable insights, making it suitable for real-world clinical deployment. Overall, this research highlights the potential of multimodal and ensemble-based intelligent systems in facilitating early cancer detection, improving clinical decision-making, and advancing next-generation healthcare analytics
Keywords: Multimodal Learning, Ensemble Learning, Cancer Diagnosis, CNN, Transformer Models, Feature Fusion, Clinical Decision Support Systems, Explainable AI