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This study presents a comprehensive method for detecting lung cancer in CT scans using the LIDC-IDRI dataset, advanced image processing, and deep learning. The procedure includes image resizing, noise reduction via median filtering, binary conversion via thresholding and refinement via morphological opening. The analysis begins with image segmentation using active contours, which is followed by feature detection using the innovative HOVG-Net model, which combines the Circular Hough Transform and VGG-19. The evaluation phase quantifies the presence of lung cancer by revealing 59 circular features covering a total area of 2916.3635. This methodology not only accurately identifies lung cancer indicators, but it also provides a clear visual representation of the findings, which contributes significantly to improved medical diagnosis and treatment planning.

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HOVG-Net: A Hybrid Model for the Early Detection of Lung Cancer in Computed Tomography

  • 会议论文

  • 发表日期：

    2026年

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  • Syed Zaheer Ahammed,
  • Radhika Baskar,
  • G. NalliniPriya

摘要

This study presents a comprehensive method for detecting lung cancer in CT scans using the LIDC-IDRI dataset, advanced image processing, and deep learning. The procedure includes image resizing, noise reduction via median filtering, binary conversion via thresholding and refinement via morphological opening. The analysis begins with image segmentation using active contours, which is followed by feature detection using the innovative HOVG-Net model, which combines the Circular Hough Transform and VGG-19. The evaluation phase quantifies the presence of lung cancer by revealing 59 circular features covering a total area of 2916.3635. This methodology not only accurately identifies lung cancer indicators, but it also provides a clear visual representation of the findings, which contributes significantly to improved medical diagnosis and treatment planning.