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Tajeddin, Golnaz, Ayyoubi Nezhad, Shima, Khatibi, Toktam, Sohrabi, Masoudreza. (1403). Proposing a novel deep method for detection and localization of anatomical landmarks from the endoscopic video frames. , 56(2), 24-40. doi: 10.22059/jac.2024.385135.1218
Golnaz Tajeddin; Shima Ayyoubi Nezhad; Toktam Khatibi; Masoudreza Sohrabi. "Proposing a novel deep method for detection and localization of anatomical landmarks from the endoscopic video frames". , 56, 2, 1403, 24-40. doi: 10.22059/jac.2024.385135.1218
Tajeddin, Golnaz, Ayyoubi Nezhad, Shima, Khatibi, Toktam, Sohrabi, Masoudreza. (1403). 'Proposing a novel deep method for detection and localization of anatomical landmarks from the endoscopic video frames', , 56(2), pp. 24-40. doi: 10.22059/jac.2024.385135.1218
Tajeddin, Golnaz, Ayyoubi Nezhad, Shima, Khatibi, Toktam, Sohrabi, Masoudreza. Proposing a novel deep method for detection and localization of anatomical landmarks from the endoscopic video frames. , 1403; 56(2): 24-40. doi: 10.22059/jac.2024.385135.1218

Proposing a novel deep method for detection and localization of anatomical landmarks from the endoscopic video frames

Journal of Algorithms and Computation
دوره 56، شماره 2، اسفند 2024، صفحه 24-40    اصل مقاله (1.66 M)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22059/jac.2024.385135.1218
نویسندگان
Golnaz Tajeddin* 1؛ Shima Ayyoubi Nezhad1؛ Toktam Khatibi1؛ Masoudreza Sohrabi2
1School of Industrial and Systems Engineering, Tarbiat Modares University (TMU), Tehran, Iran
2Gastrointestinal and liver diseases research center, Iran University of Medical Sciences (IUMS), Tehran, Iran
چکیده
Early detection of gastrointestinal cancer remains a major challenge, particularly in identifying cancerous regions at their initial stages. Anatomical landmarks are crucial for guiding physicians during endoscopic screenings, with accurate localization enhancing diagnostic precision. This study proposes a deep learning approach using convolutional neural networks (CNNs) to detect and localize anatomical landmarks in endoscopic video frames from 40 patients at Firoozgar Hospital, Tehran. Pre-processed frames were annotated with bounding boxes to highlight regions of interest. The CNN model achieved 97.0% accuracy for landmark detection and classification and an MSE of 0.004 for bounding box regression, showing promise for assisting early diagnosis.
کلیدواژه‌ها
Machine learning؛ Computer vision؛ Object Detection؛ Medical Image Analysis؛ Symptoms localization
آمار
تعداد مشاهده مقاله: 263
تعداد دریافت فایل اصل مقاله: 285





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