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<p>Deep learning techniques in image processing are gaining widespread application, with growing research in medical image analysis and diagnosis driven by advancements in image recognition technology. This study aimed to addresses the challenges of lung nodule recognition and classification using convolutional neural networks (CNN) and proposes a novel multiscale convolutional neural network (MCNN) model. The MCNN model integrates Gaussian Pyramid Decomposition (GPD) to enhance CNN-based image recognition for lung nodule detection. A practical study was conducted to apply the MCNN model, and its performance was compared with various algorithmic models and classifiers. Experimental results show that the MCNN model outperforms traditional CNN methods, particularly in detecting solid nodules and pure ground-glass nodules, with an improvement in F1 values of over 2.0%. Furthermore, the MCNN model demonstrated superior overall accuracy in lung nodule detection. These findings underline the practical implications of deep learning in advancing medical image analysis and diagnosis, offering new possibilities for improving the prognosis of lung nodule-relate diseases.</p>

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A novel method based on a multiscale convolution neural network for identifying lung nodules

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月28日

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  • Honglin Xiong,
  • Yifei Lu,
  • Junxiang Qiu,
  • Tao Wu,
  • Hong Liu,
  • Zhewei Fei,
  • Chongjun Fan,
  • Panpan Zhang

摘要

Deep learning techniques in image processing are gaining widespread application, with growing research in medical image analysis and diagnosis driven by advancements in image recognition technology. This study aimed to addresses the challenges of lung nodule recognition and classification using convolutional neural networks (CNN) and proposes a novel multiscale convolutional neural network (MCNN) model. The MCNN model integrates Gaussian Pyramid Decomposition (GPD) to enhance CNN-based image recognition for lung nodule detection. A practical study was conducted to apply the MCNN model, and its performance was compared with various algorithmic models and classifiers. Experimental results show that the MCNN model outperforms traditional CNN methods, particularly in detecting solid nodules and pure ground-glass nodules, with an improvement in F1 values of over 2.0%. Furthermore, the MCNN model demonstrated superior overall accuracy in lung nodule detection. These findings underline the practical implications of deep learning in advancing medical image analysis and diagnosis, offering new possibilities for improving the prognosis of lung nodule-relate diseases.