Version 1
: Received: 28 March 2024 / Approved: 29 March 2024 / Online: 29 March 2024 (13:51:41 CET)
How to cite:
Nuruzzaman Nobel, S.; Faruque Sifat, O.; Islam, M.R.; Sayeed, M.S.; Amiruzzaman, M. ResECA-Unet: Enhancement of GI Tract Segmentation Using an Improved U-Net Framework. Preprints2024, 2024031833. https://doi.org/10.20944/preprints202403.1833.v1
Nuruzzaman Nobel, S.; Faruque Sifat, O.; Islam, M.R.; Sayeed, M.S.; Amiruzzaman, M. ResECA-Unet: Enhancement of GI Tract Segmentation Using an Improved U-Net Framework. Preprints 2024, 2024031833. https://doi.org/10.20944/preprints202403.1833.v1
Nuruzzaman Nobel, S.; Faruque Sifat, O.; Islam, M.R.; Sayeed, M.S.; Amiruzzaman, M. ResECA-Unet: Enhancement of GI Tract Segmentation Using an Improved U-Net Framework. Preprints2024, 2024031833. https://doi.org/10.20944/preprints202403.1833.v1
APA Style
Nuruzzaman Nobel, S., Faruque Sifat, O., Islam, M.R., Sayeed, M.S., & Amiruzzaman, M. (2024). ResECA-Unet: Enhancement of GI Tract Segmentation Using an Improved U-Net Framework. Preprints. https://doi.org/10.20944/preprints202403.1833.v1
Chicago/Turabian Style
Nuruzzaman Nobel, S., MD Shohel Sayeed and Md Amiruzzaman. 2024 "ResECA-Unet: Enhancement of GI Tract Segmentation Using an Improved U-Net Framework" Preprints. https://doi.org/10.20944/preprints202403.1833.v1
Abstract
This research introduces a pioneering solution to the challenges posed by gastrointestinal tract (GI) cancer in radiation therapy, focusing on the imperative task of precise organ segmentation for minimizing radiation-induced damage. GI imaging has historically used manual demarcation, which is laborious and uncomfortable for patients. We address this by introducing the ResECA-U-Net deep learning model, a novel combination of the UNet and ResNet34 architectures. Furthermore, we further augment its functionality by incorporating the Efficient Channel Attention (ECA-Net) methodology. By utilizing data from the UW-Madison Carbone Cancer Centre, we carefully investigate several image processing techniques designed to capture critical local characteristics. With its foundation in computer vision concepts, the ResECA-U-Net model is excellent at extracting fine details from GI images. Sophisticated metrics such as intersection over union (IoU) and the dice coefficient are used to evaluate performance. Our study's outcomes demonstrate the effectiveness of the suggested method, yielding an impressive 96.27% Dice coefficient and 91.48% IoU. These results highlight the significant contribution that our strategy has made to the advancement of cancer therapy. Beyond its scientific merits, this work has the potential to significantly enhance cancer patients' quality of life and provide better long-term outcomes. Our work is a significant step towards automating and optimizing the segmentation process, which can potentially change how GI cancer is treated completely.
Keywords
U-Net; Deep learning; Transfer learning; ECA-Net; Computer vision; GI tract; Segmentation; Radiation therapy
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
