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This review article provides a comprehensive survey of lung field segmentation methods in chest radiographs, highlighting their principles, datasets, performance, and challenges. It emphasizes the importance of accurate segmentation for computer-aided diagnostic systems and discusses the evolution of techniques from heuristic methods to deep learning. The article also addresses the lack of a consolidated survey in the field and outlines the current state and future expectations of deep learning in lung fiel

Author

Kiran

Language

EN