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@article{176651,
        author = {Kalyanapu Janaki Venkatasai and Kaki Ganesha Saketh and Jetty Nithin and Dr. G Ganapathi Rao},
        title = {Lung-Retina Net: Lung Cancer and Stage Detection Using a RetinaNet with Multi-Scale Feature Fusion and Context Module},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {6767-6772},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=176651},
        abstract = {Early identification of lung cancer is crucial yet difficult, and it continues to pose a serious danger to global health. Conventional techniques like CT scans and blood tests take a long time and involve a lot of human labor. In order to lower mortality, this study suggests Lung-RetinaNet, a unique automated approach for identifying lung cancers and determining their severity. The model uses a dilated lightweight approach in the context module to improve tumor localization, especially for tiny tumors, and incorporates a multi-scale feature fusion module to augment semantic information. In comparison to current deep learning-based techniques, Lung-RetinaNet achieves good accuracy (99.8%), recall (99.3%), precision (99.4%), F1-score (99.5%), and AUC (0.989), proving its efficacy in lung cancer detection.},
        keywords = {RetinaNet, lung cancer, early detection of artificial intelligence},
        month = {April},
        }