Advanced Deep Learning Techniques for Lung Cancer Early Prediction

Computer tomography (CT) images of the lungs were vital in the early diagnosis of lung cancer, thus lowering the mortality rate and patient outcomes. As more and more medical imaging data was made available, machine learning (ML) and deep learning (DL) strategies have become useful in creating computer-aided diagnosis (CAD) systems to predict lung cancer. The paper aimed to provide a synthesized literature review of the most recent and representative research using traditional ML, U-Net-based segmentation structures, transfer learning, and more sophisticated learning structures, including multimodal and federated learning. The reviewed literature suggested that traditional ML methods based on radiomic features that were manually determined had an average classification performance but that the DL models greatly improved the performance of detecting and diagnosing tissue by automatically learning features. Specifically, 3D CNNs and attention-enhanced encoder-decoder networks performed better because they were capable of capturing volumetric and contextual data of CT scans. The transfer learning methods also enhanced the model robustness with limited training data and multimodal PET/CT models and federated learning models have resolved the issue of diagnostic reliability and patient data privacy. All in all, the integrated results demonstrated that deep learning-based CAD systems were consistently better than conventional methods and offered the way forward in the accurate and early-stage prediction of lung cancer and the reliable support of clinical decisions.