Lung Cancer Classification Using CT Scan Images Through Deep Learning And CNN Based Model

Lung cancer stands as a formidable and prevalent threat, necessitating urgent attention to early diagnosis and precise treatment to mitigate its high fatality rates. In this context, the utilization of computed tomography (CT) scans, particularly in conjunction with advanced deep learning algorithms, emerges as a powerful strategy for effective lung cancer identification. This study introduces a specialized Convolutional Neural Network (CNN) framework meticulously designed for the early detection of lung cancer through the analysis of CT scan images. Through rigorous comparative analyses with alternative models, our research highlights the CNN's superior performance, marking a substantial improvement over conventional diagnostic technique. The results accentuate the efficacy of our proposed deep learning model, solidifying its position as a more robust and potent diagnostic tool compared to prevailing approaches for the early identification of lung cancer. Future research avenues may explore the integration of larger and more diverse datasets, ensuring the model's robustness and applicability across varied clinical scenarios, ultimately advancing the landscape of lung cancer diagnostics towards improved patient outcomes and healthcare practices.