Lung Segmentation Using Proposed Deep Learning Architecture

Authors

DOI:

https://doi.org/10.3991/ijoe.v16i15.17115

Keywords:

CT images, lung segmentation, DNN, CNN, Softmax

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. 

Author Biographies

Hayder Ayad, Al-Bayan University

Hayder Ayad currently works at the College of Business Administration, Al-Bayan University. Their current project is 'Visual object categorization'. He recived his PhD. in computer science from university of technology, Iraq, 2017.

Mustafa Salam Kadhm, Imam Ja'afar Al-Sadiq University

Dr. Mustafa Salam is an associate dean at faculty of Information Technology, Imam Ja'afar Al-Sadiq University. He received his B.S. degrees in Software Engineering from Al-Mansour University College, Baghdad, Iraq in 2009 and M.S. in Information Technology from University of Tun Abdulrazak, Malaysia in 2012. Besides, he received the PhD. in computer science from University of Technology, Iraq. His research interests include Artificial Intelligence, Image Processing, Computer Vision, Pattern Recognition, and Data Mining.

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Published

2020-12-15

How to Cite

Ayad, H., Ghindawi, I. W., & Kadhm, M. S. (2020). Lung Segmentation Using Proposed Deep Learning Architecture. International Journal of Online and Biomedical Engineering (iJOE), 16(15), pp. 141–147. https://doi.org/10.3991/ijoe.v16i15.17115

Issue

Vol. 16 No. 15 (2020)

Section

Short Papers