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@article{155823,
        author = {P. Sameera and Dr. T. Panduranga Vital and A.SaiPrasanna and P.V.RajyaLaxmi and D.NityaSantoshini and G.Avanthika},
        title = {Lung Cancer Detection Using Deep Neural Networks},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {9},
        number = {2},
        pages = {26-30},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=155823},
        abstract = {As a result of uncontrollable lung cell development, lung cancer typically affects both men and women. This represents a serious respiratory issue that affects both chest inhalation and exhalation. According to a global health organisation, tobacco smoke and cigarette smoking are the leading causes of lung cancer. In comparison to other cancers, the death rate from lung cancer is increasing daily among both young and old people. The mortality rate is not yet being thoroughly monitored, despite the availability of high-tech medical facilities for effective and efficient medical care. This project proposes a system that can be used for the automatic identification of the Lung cancer from the Lung CT-Scan images. In this method, the features of the Lung CT-Scan images are extracted by using the deep convolutional neural networks (CNN) based VGG-16 model, and then the features extracted from the images are then used to train machine learning models that can accurately classify Lung CT-Scan   images into Cancer or Non-Cancer. This system enables us to detect the Lung cancer patients in a better and more accurate way.},
        keywords = {CT-SCAN, CNN, VGG-16},
        month = {},
        }