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Abstract

Lung cancer remains one of the leading causes of cancer-related deaths worldwide, with early detection being critical for improving patient outcomes. This paper presents a novel deep learning framework designed to revolutionize lung cancer detection by achieving superior accuracy and enabling early diagnosis. Leveraging advanced convolutional neural networks (CNNs) and attention mechanisms, the proposed model processes high-resolution medical imaging data, such as CT scans, to identify malignant lesions with unprecedented precision. The framework incorporates innovative preprocessing techniques, ensemble learning, and transfer learning to enhance generalizability and robustness. Evaluated on a large, publicly available dataset, the model demonstrates significant improvements in key performance metrics, including accuracy, precision, recall, and AUC-ROC, outperforming state-of-the-art methods. The results highlight the potential of this framework to assist clinicians in early and accurate lung cancer diagnosis, ultimately improving patient care and survival rates. This research paves the way for future advancements in AI-driven medical diagnostics.