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@article{161997,
        author = {Manav Karwa and Shreya Dasari and Shruthi Adepu and Shubham Karupakala and Bettam Siddardha and S Siddarth Sankar},
        title = {Lung Cancer Detection Using Machine Learning Techniques},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {10},
        number = {7},
        pages = {253-256},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=161997},
        abstract = {Lung cancer is a common and dangerous disease that claims many lives worldwide each year. Early detection is critical in improving patient prognosis and survival rates. In recent years, machine learning algorithms have showed promising results in assisting in the early identification of lung cancer through the processing of medical images such as computed tomography (CT) scans. The goal of this project is to create an innovative machine learning-based application for accurate and efficient lung cancer screening. The suggested application analyzes medical photos using cutting-edge machine learning techniques to identify probable lung cancer lesions Various machine learning models, such as convolutional neural networks (CNNs), and ensemble approaches, will be tested for their ability to differentiate between benign and malignant lung lesions. To improve performance, model parameters will be fine-tuned using cross-validation. This application will offer healthcare practitioners a streamlined interface for uploading and analyzing patient CT scans, as well as presenting detection results in an understandable format.},
        keywords = {},
        month = {},
        }