Lung Segmentation Using Proposed Deep Learning Architecture

Hayder Ayad, Ikhlas Watan Ghindawi, Mustafa Salam Kadhm

Abstract


The Prediction and detection disease in human lungs are a very critical operation. It depends on an efficient view of the CT images to the doctors. It depends on an efficient view of the CT images to the doctors. The clear view of the images to clearly identify the disease depends on the segmentation that may save people lives. Therefore, an accurate lung segmentation system from CT image based on proposed CNN architecture is proposed. The system used weighted softmax function the improved the segmentation accuracy. By experiments, the system achieved a high segmentation accuracy 98.9% using LIDC-IDRI CT lung images database. 

Keywords


CT images; lung segmentation; DNN; CNN; Softmax

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International Journal of Online and Biomedical Engineering (iJOE) – eISSN: 2626-8493
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