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Lung-Retina Net: Lung Cancer and Stage Detection Using a RetinaNet with Multi-Scale Feature Fusion and Context Module

  • Unique Paper ID: 176651
  • Volume: 11
  • Issue: 11
  • PageNo: 6767-6772
  • Abstract:
  • Early identification of lung cancer is crucial yet difficult, and it continues to pose a serious danger to global health. Conventional techniques like CT scans and blood tests take a long time and involve a lot of human labor. In order to lower mortality, this study suggests Lung-RetinaNet, a unique automated approach for identifying lung cancers and determining their severity. The model uses a dilated lightweight approach in the context module to improve tumor localization, especially for tiny tumors, and incorporates a multi-scale feature fusion module to augment semantic information. In comparison to current deep learning-based techniques, Lung-RetinaNet achieves good accuracy (99.8%), recall (99.3%), precision (99.4%), F1-score (99.5%), and AUC (0.989), proving its efficacy in lung cancer detection.
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Cite This Article

  • ISSN: 2349-6002
  • Volume: 11
  • Issue: 11
  • PageNo: 6767-6772

Lung-Retina Net: Lung Cancer and Stage Detection Using a RetinaNet with Multi-Scale Feature Fusion and Context Module

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