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An advanced ensemble deep learning framework for accurate multi-class lung cancer classification using IUNet++ and
Koteswararao Kokkila Gadda1, Lalitha Kumari Pappala1
1School of computer science and engineering, VIT-AP University, Amaravathi 522237, India.
Computational Biology and Chemistry
|June 16, 2026
Summary
This study introduces an ensemble deep learning framework for accurate lung cancer diagnosis from CT scans. The novel approach enhances image quality, improves tumor segmentation, and efficiently classifies lung cancer subtypes, achieving high diagnostic performance.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Oncology
Background:
- Lung cancer remains a leading cause of cancer mortality globally, necessitating early and precise diagnosis.
- Deep learning models show promise for lung cancer detection but often suffer from computational complexity and limited generalization.
- Existing methods require improvement in accuracy, efficiency, and robustness for clinical application.
Purpose of the Study:
- To propose an ensemble deep learning framework for automated lung cancer diagnosis from CT images.
- To enhance diagnostic accuracy and computational efficiency compared to existing methods.
- To develop a reliable computer-aided diagnostic tool for clinical decision support.
Main Methods:
- Pre-processing CT images using Gabor filtering, CLAHE, and data augmentation.
- Employing an Improved UNet++ (IUNet++) with Convolutional Block Attention Module (CBAM) for tumor segmentation.
- Feature extraction (histogram, texture, binary, RST) followed by selection using Enhanced Elephant Herding Optimization Algorithm (EEHOA).
- Classification of lung cancer into benign, malignant, and normal using a Modified ResNeXt (MResNeXt) model.
Main Results:
- The proposed framework achieved 99% accuracy, 99% precision, 99% recall, and 98.66% F1-score on the IQ-OTH/NCCD dataset.
- Outperformed several state-of-the-art methods including CNN, KNN, Modified YOLOv3, ShuffleNet, and EfficientNet.
- Demonstrated reduced model complexity and increased training efficiency through attention-guided segmentation and optimized feature selection.
Conclusions:
- The developed ensemble deep learning framework offers a practical and computationally efficient solution for multi-class lung cancer diagnosis from CT images.
- The approach shows significant potential as a dependable computer-aided diagnostic tool for clinical decision support.
- Further validation and integration into clinical workflows could improve patient survival rates through earlier and more accurate diagnosis.