An Ensemble Deep Learning Framework Integrating Swin Transformers for Robust Lung Cancer Diagnosis Using Chest CT Scans

Due primarily to delayed diagnosis, lung cancer remains one of the leading causes of cancer-related deaths worldwide. Automatic detection of lung cancer has been investigated using deep learning (DL) algorithms with chest computed tomography (CT) images, with promising results. Most current models, however, only work on small local image features, making them sensitive to noise and variations in image quality. Also, single-model methods might not ensure sufficient reliability for real clinical use. To address these limitations, a hybrid network, termed Swin-CNN-EL, is developed in this work. The proposed framework employs a Swin Transformer to extract meaningful characteristics from CT scan images, while an ensemble of three Convolutional Neural Network models—VGG19, EfficientNetB7, and Inception-v3—is utilised for classification. The outputs from these classifiers are combined using majority voting, where the final class label is determined by the prediction of the majority of models. This ensemble mechanism improves both classification reliability and overall predictive performance. Before model training, several preprocessing steps are applied to the CT images, including contrast-limited adaptive histogram equalisation (CLAHE), median filtering, and morphological enhancement. In addition, data augmentation methods are incorporated to improve the network's adaptability to diverse imaging variations and reduce overfitting. The effectiveness of the developed model is evaluated using a publicly accessible Kaggle lung cancer CT scan dataset containing 2,274 images categorised into four groups: adenocarcinoma, large cell carcinoma, squamous cell carcinoma, and normal lung tissue. For experimentation, the dataset is split into 70% training, 10% validation, and 20% test data. Furthermore, 5-fold stratified cross-validation is implemented to ensure dependable and unbiased performance assessment. Experimental findings reveal that the proposed Swin-CNN-EL architecture achieves strong classification capability, obtaining 99.6% accuracy, 99.1% precision, 98.7% recall, 98.9% F1-score, and 99.2% AUC. The narrow 95% confidence intervals additionally indicate the consistency and stability of the framework. Overall, the results confirm that the proposed approach can accurately and consistently classify lung cancer from chest CT images.