A Novel Hybrid Deep Learning Method for Early Detection of Lung Cancer Using Neural Networks

Abstract

Early detection of lung cancer remains a critical challenge due to the complexity of pulmonary nodules and limitations in conventional diagnostic systems. Existing computer-aided detection approaches rely heavily on handcrafted features and large annotated datasets, often resulting in high false-positive rates and limited generalization across diverse imaging conditions. To address these issues, this study proposes a novel hybrid deep learning framework for automated lung cancer detection using computed tomography (CT) images. The proposed model integrates convolutional neural networks with advanced architectures such as 3D-CNN and attention-based mechanisms to effectively capture both spatial and volumetric characteristics of lung nodules. Additionally, transfer learning and generative adversarial network (GAN)-based data augmentation are incorporated to mitigate data scarcity and enhance model robustness. The system performs end-to-end processing, including segmentation, feature extraction, and classification, eliminating the need for manual intervention. Experimental design focuses on improving sensitivity, specificity, and overall diagnostic accuracy while significantly reducing false positives. The proposed approach demonstrates improved adaptability across heterogeneous datasets and supports early-stage diagnosis, which is crucial for increasing patient survival rates. This framework aims to assist clinicians by providing a reliable, scalable, and efficient decision-support tool for lung cancer screening.