Md. Rashed

I am Md. Rashed, a passionate researcher in computer vision, medical image analysis, and deep learning. I hold a B.Sc. (Engr.) in Department of Information and Communication Engineering from Pabna University of Science and Technology; where I developed an SE-UNet: Spatial and Channel Refinement for Robust Multi-Anatomical Structure Segmentation in Abdominal Ultrasound.

With a strong record of publications in peer-reviewed journals such as Healthcare Technology Letters, Scientific Reports, IEEE FMLDS, and CIEES, my research focuses on deep learning for medical imaging, computer vision, and intelligent healthcare systems. I have contributed to multiple research projects, including a UGC-funded AI-based real-time fire and accident detection system, where I worked on data preprocessing, model development, and system integration.

Currently, I am working as a Research Assistant at BioRAIN Lab; and have prior experience as an Assistant Researcher at PUST, actively contributing to research in AI, Computer Vision, Biomedical Image Processing, and IoT. My work includes developing advanced architectures such as attention-based networks, fully convolutional models, and hybrid machine learning frameworks for real-world applications.

I have been awarded a Fully Funded Master’s Scholarship in Computer Science at Kocaeli University, Turkey, but the study has not commenced due to visa constraints, along with multiple University Merit Scholarships (2021–2023). Additionally, my research has received recognition through a Best Paper Award at IEEE CIEES 2025, highlighting the impact of my contributions in the field.

I am passionate about conducting impactful research and publishing in top-tier journals (TMI, PAMI, MIA, TIP) and conferences (CVPR, MICCAI, MIDL, ISBI, ICLR, ECCV). With strong expertise in Python, PyTorch, and large-scale experimentation, my goal is to advance state-of-the-art solutions in Medical Imaging, Computer Vision, and AI-driven systems.

Selected Publications

SE-UNet: Spatial and Channel Refinement for Robust Multi-Anatomical Structure Segmentation in Abdominal Ultrasound (Under Review)

Md. Rashed, Md. Imran Hossain, Md Jamil, and 4 more authors

Scientific Reports, 2026

DOI Bib PDF Code

@article{rashed2026scientificreports,
  title = {SE-UNet: Spatial and Channel Refinement for Robust Multi-Anatomical Structure Segmentation in Abdominal Ultrasound (Under Review)},
  journal = {Scientific Reports},
  pages = {},
  year = {2026},
  organization = {Nature Research},
  publisher = {Nature Research},
  author = {Rashed, Md. and Hossain, Md. Imran and Jamil, Md and Hassan, Md. Riad and Hasan, Md. Kamrul and Kavak, Adnan and Çakır, Özgür},
  doi = {},
  keywords = {},
  url = {},
}

HTL

Trustworthy Deep Feature Extraction and Ensemble-Based Machine Learning Approach for Breast Cancer Detection (Accepted not online yet)

Md. Rashed, Mohammad Kamrul Hasan, Md. Imran Hossain, and 3 more authors

Healthcare Technology Letters, 2026

DOI Bib PDF Code

@article{rashed2026htl,
  title = {Trustworthy Deep Feature Extraction and Ensemble-Based Machine Learning Approach for Breast Cancer Detection (Accepted not online yet)},
  journal = {Healthcare Technology Letters},
  pages = {},
  year = {2026},
  organization = {IET},
  publisher = {Wiley},
  author = {Rashed, Md. and Hasan, Mohammad Kamrul and Hossain, Md. Imran and Hosain, Md. Sarwar and Rahman, Abdul Hadi Abd and and Shayla Islam},
  doi = {},
  keywords = {},
  url = {},
}

CIEES

Da-RDU-Net: Dual Attention and Residual Double U-Net for Colorectal Polyp Segmentation (Best Paper Awarded)

Md. Rashed, Md Imran Hossain, and Adnan Kavak

In 2025 6th International Conference on Communications, Information, Electronic and Energy Systems (CIEES), 2025

DOI Bib PDF Code

@inproceedings{rashed2025ciees,
  title = {Da-RDU-Net: Dual Attention and Residual Double U-Net for Colorectal Polyp Segmentation (Best Paper Awarded)},
  booktitle = {2025 6th International Conference on Communications, Information, Electronic and Energy Systems (CIEES)},
  pages = {1--6},
  year = {2025},
  organization = {IEEE},
  publisher = {IEEE},
  author = {Rashed, Md. and Hossain, Md Imran and Kavak, Adnan},
  doi = {10.1109/CIEES66347.2025.11300119},
  keywords = {Image segmentation, Visualization, Accuracy, Uncertainty, Colonoscopy, Colorectal cancer, Benchmark testing, Transformers, Robustness, Real-time systems, colorectal cancer, polyp segmentation, deep learning, dual attention, medical image segmentation},
  url = {https://ieeexplore.ieee.org/abstract/document/11300119},
}

Cureus

Hybrid machine learning models for accurate type 2 diabetes mellitus prediction using a stacking classifier and a meta-model approach

Md. Rashed, Md Imran Hossain, Akif Mahdi, and 1 more author

Cureus Journal of Computer Science, 2025

DOI Bib PDF Code

@article{rashed2025hybrid,
  title = {Hybrid machine learning models for accurate type 2 diabetes mellitus prediction using a stacking classifier and a meta-model approach},
  journal = {Cureus Journal of Computer Science},
  volume = {2},
  year = {2025},
  author = {Rashed, Md. and Hossain, Md Imran and Mahdi, Akif and Mustofa, Gulam},
  doi = {10.7759/s44389-025-03135-0},
  issn = {3005-1487},
  url = {https://assets.cureusjournals.com/artifacts/upload/original_article/pdf/3135/20250304-116426-3b7a2e.pdf},
}

FMLDS

An Intelligent Video Surveillance System with Temporal-Enhanced YOLOv8 for Real-Time Fire, Accident, and License Plate Detection in Public Safety Monitoring

Md. Rashed, Md Imran Hossain, Md Zahidul Islam, and 1 more author

In 2025 IEEE International Conference on Future Machine Learning and Data Science (FMLDS), 2025

DOI Bib PDF Code

@inproceedings{rashed2025fmlds,
  title = {An Intelligent Video Surveillance System with Temporal-Enhanced YOLOv8 for Real-Time Fire, Accident, and License Plate Detection in Public Safety Monitoring},
  booktitle = {2025 IEEE International Conference on Future Machine Learning and Data Science (FMLDS)},
  pages = {139--144},
  year = {2025},
  organization = {IEEE},
  publisher = {IEEE},
  author = {Rashed, Md. and Hossain, Md Imran and Islam, Md Zahidul and Naznin, Feroza},
  doi = {10.1109/FMLDS67896.2025.00032},
  keywords = {Wavelet transforms, Visualization, Transportation, Traffic control, Wavelet analysis, Video surveillance, Real-time systems, Public security, License plate recognition, Videos, Road accident detection, Fire detection, Number plate recognition, Deep learning, CCTV footage, YOLOv8},
  url = {https://ieeexplore.ieee.org/document/11446847},
}