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@article{178660,
        author = {M. KEERTHANA and B. SURAKSHITHA and B. ARAVIND and V. ROHITH and Dr. M. DHASARATHAM},
        title = {LUNG CANCER CLASSIFICATION USING AI},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {4219-4223},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=178660},
        abstract = {With the steady rise in lung cancer cases each year, early and precise diagnosis has become increasingly critical to ensure patients receive timely and effective treatment. To enhance diagnostic accuracy, low-dose computed tomography (CT) scans are widely used, offering detailed imaging that aids in detecting lung abnormalities. A key step in this diagnostic workflow is the identification of nodular formations within the lung tissue, as these nodules can be early indicators of malignancy. In this study, the LUNA16 dataset serves as the foundation for developing an automated detection system. This dataset includes 888 CT scans, each meticulously annotated with the exact coordinates of lung nodules. To prepare the data for analysis, three-dimensional volumesor cubes are extracted from each scan, centering the nodule within the volume. These volumetric samples provide rich spatial information essential for accurate detection. A 3D Convolutional Neural Network (3D CNN) is trained on these extracted cubes to learn the distinct features of lung nodules. This deep learning model effectively captures the three-dimensional structure of the nodules, allowing for robust recognition and localization across different scans.},
        keywords = {Lung Cancer Detection, Convolution Neural Network (CNN), VGG16, CT Scan Classification, Deep Learning,Medical Image Analysis.},
        month = {May},
        }