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@article{179698,
        author = {Shree Divya B and YaminiPriya K and N SriPriya and Shivani M},
        title = {Lung cancer detection using CNN},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {7755-7757},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=179698},
        abstract = {Lung cancer is a major cause of death globally, often due to delayed or inaccurate diagnosis. Traditional methods rely on manual image analysis, which is time-consuming and prone to human error. This project proposes an automated lung cancer detection system using Convolutional Neural Networks (CNNs) and transfer learning to improve diagnostic accuracy and speed. The system classifies CT and X-ray images into four categories: Normal, Adenocarcinoma, Large Cell Carcinoma, and Squamous Cell Carcinoma. A Raspberry Pi–based web platform enables users to upload medical images and receive real-time predictions. This approach reduces human effort, supports healthcare professionals, and ensures faster, more reliable diagnoses. It offers a scalable and efficient solution to enhance early detection and improve patient outcomes.},
        keywords = {lung cancer detection, convolutional neural network (CNN), medical imaging, transfer learning, deep learning, classification, automated diagnosis, CT scan, X-ray analysis, healthcare AI.},
        month = {May},
        }