Comprehensive and Comparative Framework for Lung Cancer Detection Using CNN

In this paper, we present an AI-driven Lung Cancer Detection System using CNN Lung cancer remains a leading cause of mortality worldwide, emphasizing the urgent need for early and accurate detection to improve patient outcomes. In this study, we delve into the effectiveness of Convolutional Neural Networks (CNNs), specifically ResNet50, in de tecting lung cancer from medical imaging data, particularly Computed Tomography (CT) scans. We investigate how the ResNet50 architecture, known for its deep residual connec tions, is optimally suited for analyzing image data and extracting crucial features necessary for precise diagnosis. Our focus extends to detailing the training methodology for CNNs, especially ResNet50, in this specific context, emphasizing the importance of meticulous data preparation and the evaluation of key performance metrics such as accuracy, sensitivity, and specificity. Through an extensive review of existing research, we highlight the promising potential of CNNs, with some studies reporting accuracies exceeding 90%, and the added benefits of utilizing ResNet50 in achieving higher model robustness and generalization. De spite these encouraging results, we acknowledge significant challenges such as class imbalance and the need for model generalizability across diverse patient populations and imaging con ditions. Looking ahead, we propose several avenues for further enhancement, including the exploration of 3D CNNs, which may better capture spatial information inherent in volumet ric medical imaging data like CT scans. Additionally, we advocate for the development of strategies to address data limitations, ensuring the robustness and reliability of CNN mod els in real-world clinical settings. Through this comprehensive study, we aim to underscore the transformative impact of ResNet50-powered CNNs in enabling earlier diagnoses of lung cancer, ultimately leading to improved patient care and outcomes.