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Title: | Pneumothorax Segmentation Using Low-Cost Computing Method |
Other Titles: | 2024 8th International Conference on Information Technology (InCIT) การประชุมวิชาการระดับประเทศทางด้านเทคโนโลยีสารสนเทศ (InCit2024 Conference) |
Authors: | Lucksanan Sukaroj Woranuch Meepoomroo Yuwathida Chiwpreechar Noppamas Akarachantachote Prarinya Siritanawan Sila Temsiririrkkul ลักษณ์นันท์ ศุขโรจน์ วรนุช มีภูมิรู้ ยุวธิดา ชิวปรีชา นพมาศ อัครจันทโชติ ศิลา เต็มศิริฤกษ์กุล Huachiew Chalermprakiet University. Department of Science and Technology. Undergraduate Student Huachiew Chalermprakiet University. Department of Science and Technology Huachiew Chalermprakiet University. Department of Science and Technology Huachiew Chalermprakiet University. Department of Science and Technology Shinshu University. Graduate School of Science and Technology Huachiew Chalermprakiet University. Department of Science and Technology |
Keywords: | Pneumothorax ภาวะปอดรั่ว Deep learning (Machine learning) การเรียนรู้เชิงลึก (การเรียนรู้ของเครื่อง) Image segmentation การแยกข้อมูลภาพ Dice Similarity Coefficient ค่าสัมประสิทธิ์ความคล้ายคลึงของไดซ์ Chest – Radiography ทรวงอก – การบันทึกภาพด้วยรังสี Chest X-ray Uniform Local Binary Patterns Pattern recognition systems การรู้จำรูปแบบ Artificial Intelligence ปัญญาประดิษฐ์ |
Issue Date: | 2024 |
Abstract: | Pneumothorax, a life-threatening condition characterized by air leakage in the pleural space, presents a significant diagnostic challenge in chest X-rays (CXR). Deep learning methods have shown promise in medical image segmentation, but they face limitations due to data scarcity and computational demands. This study investigates the applicability of traditional segmentation techniques, including Uniform Local Binary Patterns (ULBP), filtering, clustering, and manual pattern selection, for pneumothorax segmentation in CXR images. Our results demonstrate that these traditional methods yield significantly lower Dice coefficient scores (below 0.20) compared to the desired threshold, indicating their limitations in accurately segmenting pneumothorax. These findings highlight the challenges associated with traditional approaches for pneumothorax segmentation and emphasize the need for more advanced techniques, such as deep learning, to address the complexities of this medical imaging task. |
Description: | 2024 8th International Conference on Information Technology (InCIT) (การประชุมวิชาการระดับประเทศทางด้านเทคโนโลยีสารสนเทศ (InCit2024 Conference)) 14-15 November 2024 at Chonburi, Thailand, p. 167. DOI: 10.1109/InCIT63192.2024.10810533 |
URI: | https://has.hcu.ac.th/jspui/handle/123456789/4514 |
Appears in Collections: | Science and Technology - Proceeding Document |
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Pneumothorax-Segmentation-Using-Low-Cost-Computing-Method.pdf | 79.89 kB | Adobe PDF | View/Open |
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