Vol. 28 No. 3 (2025) Cover Image
Vol. 28 No. 3 (2025)

Published: September 30, 2025

Pages: 403-419

Articles

A Review on Automated Segmentation of Lung Lesions in Chest CT Scans Using Hybrid Approaches

Raed Hamid Lateef 1 Ahmed Hussein 2
1 Ministry of Health, Wassit Governorate Health Directorate, Al-Kut, Iraq.
2 Department of Biomedical Engineering , College of Engineering, Al-Nahrain University, Baghdad, Iraq.
History
  • Received: November 17, 2024
  • Revised: February 5, 2025
  • Accepted: February 20, 2025
  • Available Online: September 29, 2025
DOI

https://doi.org/10.29194/NJES.28030403

Abstract

One of the most common causes of mortality worldwide is Lung cancer, an early diagnosis crucial for a patient’s survival and recovery. Automated segmentation of lung lesions in chest CT has become a pre-eminent focal point for research, particularly with the development of hybrid methods combining traditional image processing with advanced deep learning methods such as CNN. These hybrid approaches aim to minimize individual methods limitations by controlling their merge strengths to enhance segmentation efficiency, precision, and clinical utility. This review comprehensively analyzes different hybrid techniques, such as deep learning improved by rule-based systems, multi-scale feature extraction, and ensemble learning. As well as inspect their clinical effect, particularly in improving diagnostic accuracy and optimizing treatment procedures. Despite their possibility, these approaches still face significant challenges, such as computational complexity, data requirements, and the requirement for explainable AI (XAI). Upcoming advancements in lung lesion segmentation will focus on refining these models to achieve faster processing, improved accuracy, and integration with diagnostic tools to protect transparency and ethical considerations.

Keywords

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