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<p>Segmentation plays a key role in designing a CAD system. Therefore, we need to implement highly accurate image segmentation to extract the shape of the lung from X-rays. Lung segmentation is challenging due to significant shape variations and unclear lung regions caused by severe lung diseases. Many CNN models give better results in segmentation, but every model has its limitations. Our paper proposes a new, improved model that builds upon the base model, SegNet. SegNet is the best model for semantic segmentation. Our proposed model is a combination of SegNet and ResNet 50. We are replacing the encoder of the SegNet with ResNet50. We modified the base model to get better performance and results. We utilized the Shenzhen dataset, a publicly available dataset, for training and testing the model. The model performance is evaluated in terms of global accuracy, Jaccard index, and dice similarity coefficient. The proposed model achieved a global accuracy of 97.73%, a Jaccard index of 97.32%, a Dice similarity coefficient of 97.33%, a precision of 98.74%, a recall of 98.58%, and an <i>F</i>1-score of 97.37%. The experimental results showed that our model performed better than the other models in terms of global accuracy, Jaccard index, precision, recall, <i>F</i>1-score, and dice similarity coefficient.</p>

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Advanced semantic lung segmentation with a hybrid SegNet-ResNet50 network

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月13日

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  • Mohammad Farukh Hashmi,
  • M. Devipriyanka,
  • B. Raghavaram,
  • Amreen Aijaz Husain

摘要

Segmentation plays a key role in designing a CAD system. Therefore, we need to implement highly accurate image segmentation to extract the shape of the lung from X-rays. Lung segmentation is challenging due to significant shape variations and unclear lung regions caused by severe lung diseases. Many CNN models give better results in segmentation, but every model has its limitations. Our paper proposes a new, improved model that builds upon the base model, SegNet. SegNet is the best model for semantic segmentation. Our proposed model is a combination of SegNet and ResNet 50. We are replacing the encoder of the SegNet with ResNet50. We modified the base model to get better performance and results. We utilized the Shenzhen dataset, a publicly available dataset, for training and testing the model. The model performance is evaluated in terms of global accuracy, Jaccard index, and dice similarity coefficient. The proposed model achieved a global accuracy of 97.73%, a Jaccard index of 97.32%, a Dice similarity coefficient of 97.33%, a precision of 98.74%, a recall of 98.58%, and an F1-score of 97.37%. The experimental results showed that our model performed better than the other models in terms of global accuracy, Jaccard index, precision, recall, F1-score, and dice similarity coefficient.