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Finding lung cancer frequently depends on analyzing small nodules in CT scans, which requires accurate segmentation techniques that go beyond basic image classification methods. Although current techniques that employ alternative architectures may attain respectable precision, they frequently encounter issues related to restricted CT scan datasets and scalability, impeding their practical utility. By combining the Luna16 dataset along with the Efficient U-Net architecture, which is utilized to achieve superior performance on tasks such as image classification while employing low computational resources, our study addresses the urgent need for improved lung cancer detection. This means that excellent precision can be accomplished about a few parameters and calculations are less expensive compared to various models. Our suggested model ensures sharper and more precise nodule segmentation in CT scans by excelling in feature extraction and resisting vanishing gradients. A varied and annotated benchmark, the Luna16 dataset, enables thorough learning and adaptation to different nodule kinds and imaging settings.

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A Novel Approach for Effective U-Net for Perceptive Lung Cancer Identification in CT Scan Gradient Pictures

  • 会议论文

  • 发表日期：

    2025年

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  • S. Ashwini,
  • Jonnadula Narasimharao,
  • Bobbillapati Prasad,
  • B. K. Chinna Maddileti

摘要

Finding lung cancer frequently depends on analyzing small nodules in CT scans, which requires accurate segmentation techniques that go beyond basic image classification methods. Although current techniques that employ alternative architectures may attain respectable precision, they frequently encounter issues related to restricted CT scan datasets and scalability, impeding their practical utility. By combining the Luna16 dataset along with the Efficient U-Net architecture, which is utilized to achieve superior performance on tasks such as image classification while employing low computational resources, our study addresses the urgent need for improved lung cancer detection. This means that excellent precision can be accomplished about a few parameters and calculations are less expensive compared to various models. Our suggested model ensures sharper and more precise nodule segmentation in CT scans by excelling in feature extraction and resisting vanishing gradients. A varied and annotated benchmark, the Luna16 dataset, enables thorough learning and adaptation to different nodule kinds and imaging settings.