An advanced ensemble deep learning framework for accurate multi-class lung cancer classification using IUNet++ and...

Early and precise diagnosis is crucial for increasing patient survival because lung cancer is still one of the top causes of cancer-related death globally. Even while deep learning-based methods have shown encouraging results in the research of lung cancer, many of the existing models have significant computational complexity, overfitting, and limited capacity for generalization. An ensemble deep learning framework for automated lung cancer diagnosis from CT scan images is proposed in this research. The CT images are first pre-processed utilizing Gabor filtering, CLAHE-based contrast enhancement, and data augmentation to increase image quality and address class imbalance. An Improved UNet++ (IUNet++) model combined with a Convolutional Block Attention Module (CBAM) is then used to segment the lung tumor region in order to improve feature representation and localization accuracy. The most informative features are then selected using the Enhanced Elephant Herding Optimization Algorithm (EEHOA) after histogram, texture, binary, and rotational-scale-translational (RST) features are extracted. Lastly, lung cancer is classified into benign, malignant, and normal categories using a Modified ResNeXt (MResNeXt) model that incorporates Leaky ReLU and an improved layer structure. The IQ-OTH/NCCD lung cancer CT dataset, which is accessible to the public, was used for the experiments. 99% accuracy, 99% precision, 99% recall, and 98.66% F1-score were attained with the proposed framework. The proposed approach beat a number of contemporary state-of-the-art techniques, including CNN, KNN, Modified YOLOv3, ShuffleNet, and EfficientNet-based models, according to a comparative analysis. Additionally, compared to traditional deep learning techniques, the incorporation of attention-guided segmentation, better feature selection, and an improved ResNeXt architecture decreased model complexity and increased training efficiency. For multi-class lung cancer diagnosis using CT images, the proposed ensemble framework offers a practical and computationally efficient solution. The outcomes of the experiments show that it has the potential to be a dependable computer-aided diagnostic tool for clinical decision support.