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

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