International Journal of Science, Engineering and TechnologyAuthors: Asscoiate Professor Mrs.C.Radha, Mr.R.Midunkumar, Mr.C.Mani, Mr.B.Mohanraj
Abstract: Lung cancer remains one of the leading causes of cancer-related mortality worldwide, necessitating the development of effective diagnostic and predictive tools. This paper explores the application of deep learning techniques for the prediction and classification of lung cancer, leveraging advancements in artificial intelligence to enhance early detection and improve patient outcomes. We provide a comprehensive overview of various deep learning architectures, particularly Convolutional Neural Networks (CNNs), and their efficacy in analyzing medical imaging modalities such as computed tomography (CT) scans and chest X-rays. The study highlights preprocessing methods, feature extraction techniques, and evaluation metrics that are critical for model performance. Finally, we discuss future directions for research, emphasizing the integration of deep learning with emerging technologies to further enhance diagnostic capabilities in oncology. This work aims to contribute to the ongoing efforts in utilizing artificial intelligence for improving lung cancer detection and management.
DOI: http://doi.org/10.61463/ijset.vol.13.issue3.184
