Publication: Improving chest pathologies detection from chest x-ray with deep learning using transfer learning and image enhancement
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Issued Date
2022-12-16
Resource Type
Language
eng
File Type
application/pdf
Access Rights
Open Access
Rights
ผลงานนี้เผยแพร่ภายใต้ สัญญาอนุญาตครีเอทีฟคอมมอนส์แบบ แสดงที่มา-ไม่ใช้เพื่อการค้า-ไม่ดัดแปลง 4.0 (CC BY-NC-ND 4.0)
Rights Holder(s)
Srinakharinwirot University
Suggested Citation
Tanabut Taksinavongskul, ธนบุตร ทักษิณาวงศ์สกุล (2022). Improving chest pathologies detection from chest x-ray with deep learning using transfer learning and image enhancement. Retrieved from: https://hdl.handle.net/20.500.14740/54133
Alternative Title(s)
Improving chest pathologies detection from chest x-ray with deep learning using transfer learning and image enhancement
Advisor(s)
Organization
Abstract
This research is concerned with chest radiography, which is essential for doctors to determine and follow up on lung disease. However, practicing radiologists have an insufficient ability to identify diseases in chest x-ray images. Therefore, the researchers developed deep-learning models to mitigate this problem, and CheXNet is one of the state-of-the-art models that can detect 14 lung pathologies. This research applied six image enhancement techniques to the x-ray images before using ChexNet to improve detection performance. The six techniques consisted of Gamma, Complement, HE, CLAHE, BCET, and MMCS. In addition, we studied the effectiveness of using a single enhancement technique (single channel) and a combination of them to the original image (multi-channel). Gamma gave the highest and most stable detection improvement using a single enhancement technique at 0.628% AUCROC in 14 diseases. Combining the original image, Gamma-enhanced image, and CLAHE-enhanced image shows 0.7% AUCROC improvement for 14 diseases. Moreover, this combination offers outstanding Pneumonia detection, which is 2% more than CheXNet.
This research is concerned with chest radiography, which is essential for doctors to determine and follow up on lung disease. However, practicing radiologists have an insufficient ability to identify diseases in chest x-ray images. Therefore, the researchers developed deep-learning models to mitigate this problem, and CheXNet is one of the state-of-the-art models that can detect 14 lung pathologies. This research applied six image enhancement techniques to the x-ray images before using ChexNet to improve detection performance. The six techniques consisted of Gamma, Complement, HE, CLAHE, BCET, and MMCS. In addition, we studied the effectiveness of using a single enhancement technique (single channel) and a combination of them to the original image (multi-channel). Gamma gave the highest and most stable detection improvement using a single enhancement technique at 0.628% AUCROC in 14 diseases. Combining the original image, Gamma-enhanced image, and CLAHE-enhanced image shows 0.7% AUCROC improvement for 14 diseases. Moreover, this combination offers outstanding Pneumonia detection, which is 2% more than CheXNet.
This research is concerned with chest radiography, which is essential for doctors to determine and follow up on lung disease. However, practicing radiologists have an insufficient ability to identify diseases in chest x-ray images. Therefore, the researchers developed deep-learning models to mitigate this problem, and CheXNet is one of the state-of-the-art models that can detect 14 lung pathologies. This research applied six image enhancement techniques to the x-ray images before using ChexNet to improve detection performance. The six techniques consisted of Gamma, Complement, HE, CLAHE, BCET, and MMCS. In addition, we studied the effectiveness of using a single enhancement technique (single channel) and a combination of them to the original image (multi-channel). Gamma gave the highest and most stable detection improvement using a single enhancement technique at 0.628% AUCROC in 14 diseases. Combining the original image, Gamma-enhanced image, and CLAHE-enhanced image shows 0.7% AUCROC improvement for 14 diseases. Moreover, this combination offers outstanding Pneumonia detection, which is 2% more than CheXNet.
Degree Name
MASTER OF SCIENCE (M.Sc.)
Degree Level
-
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Degree Discipline
Department Of Computer Science
Degree Grantor(s)
Srinakharinwirot University