Journal of Algorithms and Computation
Open Access

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

Document Type : Research Paper

Authors

Golnaz Tajeddin 1
Shima Ayyoubi Nezhad 1
Toktam Khatibi 1
Masoudreza Sohrabi 2

1 School of Industrial and Systems Engineering, Tarbiat Modares University (TMU), Tehran, Iran

2 Gastrointestinal and liver diseases research center, Iran University of Medical Sciences (IUMS), Tehran, Iran

https://doi.org/10.22059/jac.2024.385135.1218
Abstract
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.

Keywords

Machine learning
Computer vision
Object Detection
Medical Image Analysis
Symptoms localization
Journal of Algorithms and Computation
Volume 56, Issue 2
December 2024
Pages 24-40

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