Optimizing TEM Image Segmentation: Advancements in DRU-Net Architecture with Dense Residual Connections and Attention Mechanisms

Authors

  • M. Nagaraju Naik Department of ECE, CMR College of Engineering & Technology, Hyderabad, Telangana, India
  • Nagajyothi Dimmita Department of ECE, Vardhaman College of Engineering, Shamshabad, Hyderabad, Telangana, India
  • Vijayalakshmi Chintamaneni Department of ECE, Vignan Institute of Technology and Science, Hyderabad, Telangana, India
  • P. Srinivasa Rao Department of ECE, CVR College of Engineering, Hyderabad, Telangana State, India
  • Nagalingam Rajeswaran Department of EEE, Malla Reddy College of Engineering, Hyderabad, Telangana, India
  • Amar Y. Jaffar Computer and Network Engineering Department, College of Computing, Umm Al-Qura University, Makkah, 21955, Saudi Arabia
  • Fahd M. Aldosari Computer and Network Engineering Department, College of Computing, Umm Al-Qura University, Makkah, 21955, Saudi Arabia
  • Wesam N. Eid Cyber Security Department, College of Computing, Umm Al-Qura University, Makkah, 21955, Saudi Arabia
  • Ayman A. Alharbi Computer and Network Engineering Department, College of Computing, Umm Al-Qura University, Makkah, 21955, Saudi Arabia
Volume: 14 | Issue: 4 | Pages: 15821-15828 | August 2024 | https://doi.org/10.48084/etasr.7994

Abstract

This study introduces an innovative enhancement to the U-Net architecture, termed Modified DRU-Net, aiming to improve the segmentation of cell images in Transmission Electron Microscopy (TEM). Traditional U-Net models, while effective, often struggle to capture fine-grained details and preserve contextual information critical for accurate biomedical image segmentation. To overcome these challenges, Modified DRU-Net integrates dense residual connections and attention mechanisms into the U-Net framework. Dense connections enhance gradient flow and feature reuse, while residual connections mitigate the vanishing gradient problem, facilitating better model training. Attention blocks in the up-sampling path selectively focus on relevant features, boosting segmentation accuracy. Additionally, a combined loss function, merging focal loss and dice loss, addresses class imbalance and improves segmentation performance. Experimental results demonstrate that Modified DRU-Net significantly enhances performance metrics, underscoring its effectiveness in achieving detailed and accurate cell image segmentation in TEM images.

Keywords:

DRU Net, U-Net, dense connections, attention blocks, SEM images

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References

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How to Cite

[1]
Naik, M.N., Dimmita, N., Chintamaneni, V., Rao, P.S., Rajeswaran, N., Jaffar, A.Y., Aldosari, F.M., Eid, W.N. and Alharbi, A.A. 2024. Optimizing TEM Image Segmentation: Advancements in DRU-Net Architecture with Dense Residual Connections and Attention Mechanisms. Engineering, Technology & Applied Science Research. 14, 4 (Aug. 2024), 15821–15828. DOI:https://doi.org/10.48084/etasr.7994.

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