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<p>Image processing plays a crucial role in early disease detection, particularly in the medical field. This paper presents an automated detection system for analyzing CT scans to identify lung cancer stages accurately. Lung cancer, including small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC), is a severe disease with complex diagnostic challenges. Our system effectively detects tumors in both early and advanced stages and differentiates between SCLC and NSCLC. It comprises four phases: pre-processing, image enhancement, image segmentation, and classification. Tested on 258 images from a public dataset of 12,645 and data from 50 patients provided by VIA and I-ELCAP, the system achieved a 97.22% sensitivity with no false positives, showcasing its diagnostic accuracy.</p>

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CT-Based Automated System for Detecting and Staging Lung Cancer with High Sensitivity

  • 期刊论文

  • 发表日期：

    2026年3月6日

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  • Sakinah Mohd Shukri,
  • S. Srinadh Raju,
  • Shreenidhi H. S.,
  • Meenakshi Garg,
  • Mandeep Kaur Chohan,
  • Abhilasha Jadhav,
  • Ahmed Alkhayyat,
  • Sanjeev Kumar Shah

摘要

Image processing plays a crucial role in early disease detection, particularly in the medical field. This paper presents an automated detection system for analyzing CT scans to identify lung cancer stages accurately. Lung cancer, including small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC), is a severe disease with complex diagnostic challenges. Our system effectively detects tumors in both early and advanced stages and differentiates between SCLC and NSCLC. It comprises four phases: pre-processing, image enhancement, image segmentation, and classification. Tested on 258 images from a public dataset of 12,645 and data from 50 patients provided by VIA and I-ELCAP, the system achieved a 97.22% sensitivity with no false positives, showcasing its diagnostic accuracy.