Optimized Lung Cancer Identification in CT Imaging: A Synergistic Deep Learning Approach with Residual U-Net Segmentation and Swin Transformer Feature Extraction

Lung cancer remains a leading cause of cancer-related mortality globally, with high fatality rates often due to late-stage diagnosis. This research explores the efficacy of computer tomography (CT) imaging in the early detection of lung cancer. CT imaging, known for its high-resolution capabilities, facilitates the early identification of small nodules and abnormalities, providing detailed visualization of lung structures. This allows for the detection of minute changes that may indicate cancer. The investigation addresses the critical health issue, aiming to reduce the significant mortality and morbidity associated with lung cancer through improved early detection methods. The suggested ensemble method starts with segmentation, using residual-UNet and U-Net with DenseNet models to choose the region of interest (ROI). Following this, the Swin Transformer is employed to extract intricate features from the segmented CT images, leveraging its state-of-the-art capabilities. Principal component analysis (PCA) is implemented as a dimensionality reduction technique to optimize computational efficiency and improve feature selection. Furthermore, the DenseNet-121 and ResNet-101 models are employed to precisely identify lung nodule patterns. The investigation found that the residual U-Net model, with a dice coefficient and accuracy of 0.912 and 93.64%, respectively, is a superior method for segmenting lung nodule regions in CT images. The Swin Transformer successfully identified and extracted 215 distinct features from the segmented data obtained from the segmented lung regions. The PCA decreases the number of features extracted by the swin transformer. With an accuracy of 98.01%, an F1 score of 93.71%, and a dice coefficient of 0.938, the residual U-Net + ResNet-101 ensemble model did the best job of finding lung nodules. The outcomes demonstrate the superior performance of the proposed ensemble models compared to each other, making them the most suitable choice for lung cancer identification.