Deep learning ensemble approach with explainable AI for lung and colon cancer classification using advanced hyperparameter tuning

Lung and colon cancers are leading contributors to cancer-related fatalities globally, distinguished by unique histo-
pathological traits discernible through medical imaging. Effective classification of these cancers is critical for accurate
diagnosis and treatment. This study addresses critical challenges in the diagnostic imaging of lung and colon cancers,
which are among the leading causes of cancer-related deaths worldwide. Recognizing the limitations of existing diag-
nostic methods, which often suffer from overfitting and poor generalizability, our research introduces a novel deep
learning framework that synergistically combines the Xception and MobileNet architectures. This innovative ensem-
ble model aims to enhance feature extraction, improve model robustness, and reduce overfitting.
Our methodology involves training the hybrid model on a comprehensive dataset of histopathological images,
followed by validation against a balanced test set. The results demonstrate an impressive classification accuracy
of 99.44%, with perfect precision and recall in identifying certain cancerous and non-cancerous tissues, marking a sig-
nificant improvement over traditional approach.
The practical implications of these findings are profound. By integrating Gradient-weighted Class Activation Map-
ping (Grad-CAM), the model offers enhanced interpretability, allowing clinicians to visualize the diagnostic reasoning
process. This transparency is vital for clinical acceptance and enables more personalized, accurate treatment planning.
Our study not only pushes the boundaries of medical imaging technology but also sets the stage for future research
aimed at expanding these techniques to other types of cancer diagnostics.