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@article{205282,
        author = {Dr.S.Jeyakumar and Ms.S.Jenolin Aiswarya},
        title = {Federated Learning Based Lung Cancer Detection using VGG16 and ResNet50 Models},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {1},
        pages = {6056-6060},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=205282},
        abstract = {Lung cancer remains one of the leading causes of cancer-related mortality worldwide. Computed Tomography (CT) imaging is widely used for diagnosis; however, manual analysis is time-consuming and highly dependent on radiologist expertise. Traditional centralized training of medical data additionally raises significant privacy concerns. This paper proposes a hybrid federated learning framework for lung cancer classification using a VGG16 deep learning model. The system enables decentralized training across multiple clients while preserving patient data privacy through the Federated Averaging (FedAvg) algorithm. The final global model classifies CT scan images into three categories—Benign, Malignant, and Normal—achieving an overall accuracy of 92%, outperforming centralized VGG16 (89%), centralized ResNet50 (84%), and federated ResNet50 (91.34%) baselines. Grad-CAM visualization is incorporated to improve model interpretability and clinical trust.},
        keywords = {Lung Cancer, CT Imaging, Federated Learning, VGG16, Deep Learning, Medical Image Classification, Privacy Preservation, FedAvg Algorithm, Clinical Decision Support.},
        month = {June},
        }