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Throughout the world lung cancer has become the reason for cause of death from cancer. Its early detection may become important for its effective treatment. CT scans can help in the early detection. This paper aims at explaining how deep learning models, in the form of Convolutional Neural Networks (CNN) and Xception, can find lung cancer with excellent accuracy from CT scan images. We mainly test performance metrics in terms of accuracy, area under Curve, recall, and loss. Our results obtained have shown that the Xception outperformed the traditional CNN, these criteria, promising performance: accuracy 96.35%, area under Curve of 99.60%, recall of 94.80%, and a loss of 0.220.

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Lung Cancer Prediction Through Using CNN and Xception Based Transfer Learning

  • 会议论文

  • 发表日期：

    2025年

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  • Kanishq Kadam,
  • Kamlesh Chandravanshi

摘要

Throughout the world lung cancer has become the reason for cause of death from cancer. Its early detection may become important for its effective treatment. CT scans can help in the early detection. This paper aims at explaining how deep learning models, in the form of Convolutional Neural Networks (CNN) and Xception, can find lung cancer with excellent accuracy from CT scan images. We mainly test performance metrics in terms of accuracy, area under Curve, recall, and loss. Our results obtained have shown that the Xception outperformed the traditional CNN, these criteria, promising performance: accuracy 96.35%, area under Curve of 99.60%, recall of 94.80%, and a loss of 0.220.