Semantic Segmentation of Lungs using U-Net

Abstract

Semantic segmentation of medical images is crucial for aiding radiologists and clinicians in accurately diagnosing conditions and planning treatments. This research introduces an innovative technique for the semantic segmentation of lungs using a U-Net design. The U-Net model demonstrates outstanding performance in various medical image segmentation tasks by effectively capturing both local and global features. Additionally, Res- U-Net architecture is harnessed to identify lung regions from lungs CT images, with a focus on improving the accuracy and efficiency of the segmentation process. Python is utilized for both designing and executing the algorithm in this work. Google Colab, a popular platform, is being used for its computational resources and collaboration features. The datasets were sourced from Iraq-Oncology Teaching Hospital with 70% of the data allocated for training and 30% for testing.