Deep learning ensemble approach with explainable AI for lung and colon cancer classification using advanced hyperparameter tuning
Open accessAbstract
Lung and colon cancers are leading contributors to cancer-related fatalities globally, distinguished by unique histopathological 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 diagnostic 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 ensemble 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 significant improvement over traditional approach.The practical implications of these findings are profound. By integrating Gradient-weighted Class Activation Mapping (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.
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Grad-CAM generates heatmaps showing which regions influenced a model's decision, highlighting specific areas of a mammogram that suggest malignancy or pathology features indicative of aggressive disease.
“For imaging applications, techniques like Grad-CAM generate heatmaps showing which regions influenced a model’s decision, highlighting specific areas of a mammogram that suggest malignancy or pathology features indicative of aggressive disease [158,159,160,161].”
From Artificial Intelligence in Clinical Oncology: From Productivity Enhancement to Creative Discovery (2025) · cites this paper as [158]
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Artificial Intelligence in Clinical Oncology: From Productivity Enhancement to Creative Discovery
Kuno, M., Osumi, H., Udagawa, S., Yoshikawa, K., Ooki, A., Shinozaki, E., Ishikawa, T., Oba, J., Yamaguchi, K., & Sakurada, K. (2025) · Current Oncology · doi:10.3390/curroncol32110588 · Cited by 7 · see citing works
- Authors
- Vanitha, K., R, M. T., Sree, S. S., & Guluwadi, S.
- Affiliations
- Karpagam Academy of Higher Education (IN)
- Vels University (IN)
- Adama Science and Technology University (ET)
- Corresponding author
- Guluwadi, Suresh
- Venue
- BMC Medical Informatics and Decision Making
- Published
- 2024-08-07
- Volume
- 24
- Issue
- 1
- Pages
- 222-222
- ISSN
- 1472-6947 (ISSN-L)
- PMC
- PMC11304580
- OpenAlex
- W4401398445
- Cited by
- 62
- References
- 30
- Citations by year
- 2026: 33 · 2025: 27 · 2024: 2
- FWCI
- 9.15
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- BMC Medical Informatics and Decision Making (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Keywords
- lung cancer classification, colon cancer classification, histopathological image classification, ensemble deep learning, Xception, MobileNet, Grad-CAM, explainable AI, hyperparameter optimization, overfitting reduction, cancer diagnosis
- MeSH terms
- Deep Learning, Artificial Intelligence, Colonic Neoplasms, Humans, Lung Neoplasms
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