Published Paper Details:

One of the main causes of cancer-related mortality is lung cancer, which presents a serious healthcare issue. To address this issue, deep learning and AI technologies are being leveraged to enhance early detection and diagnosis. These innovative approaches analyze medical imaging data, such as CT scans, to improve accuracy and enable timely interventions, ultimately contributing to more effective and personalized treatments for lung cancer patients. Deep learning methodologies have been integrated to ascertain the presence of lung cancer in individuals by deriving relevant characteristics from CT-Scan images sourced from a data repository. These predictive systems not only identify lung cancer but also facilitate the analysis of inherent behavioral patterns within the images, offering additional insights. This study looks into how machine learning (ML), deep learning (DL), and artificial intelligence (AI) can improve lung cancer diagnosis, particularly through the analysis of medical imaging data, such as CT scans and by doing so focuses on a method for more precise early detection for lung cancer. In doing so this research paper proposes a novel algorithm, the Hybrid Deep Neural Network-Convolutional Neural Network (hDCN). Evaluation through standard statistical techniques reveals an accuracy rate of 69.024% and an impressive precision of 94%, highlighting the hDCN algorithm's potential in transforming early detection strategies for lung cancer.