Publications

You can also find my articles on my Google Scholar profile.

Uncertainty Driven Bottleneck Attention U-Net For Organ at Risk Segmentation

Published in 2024 IEEE International Symposium on Biomedical Imaging (ISBI), 2024

This paper introduces a multiple decoder U-net architecture for organ-at-risk (OAR) segmentation in CT images, utilizing segmentation disagreement as attention and a CT intensity integrated regularization loss for enhanced accuracy. The proposed model shows improved performance on two OAR challenge datasets.

Recommended citation: A. Nazib, R. Hassan, Z. Islam and C. Fookes, "Uncertainty Driven Bottleneck Attention U-Net For Organ at Risk Segmentation," 2024 IEEE International Symposium on Biomedical Imaging (ISBI), Athens, Greece, 2024, pp. 1-5, doi: 10.1109/ISBI56570.2024.10635587. https://ieeexplore.ieee.org/document/10635587

UDBRNet: A novel uncertainty driven boundary refined network for organ at risk segmentation

Published in PLOS ONE, 2024

In this paper, we propose a novel convolution neural network based uncertainty-driven boundary-refined segmentation network (UDBRNet) that segments the organs from CT images. The CT images are segmented first and produce multiple segmentation masks from multi-line segmentation decoder. Uncertain regions are identified from multiple masks and the boundaries of the organs are refined based on uncertainty data.

Recommended citation: Hassan R, Mondal MRH, Ahamed SI (2024) UDBRNet: A novel uncertainty driven boundary refined network for organ at risk segmentation. PLOS ONE 19(6): e0304771. https://doi.org/10.1371/journal.pone.0304771 https://doi.org/10.1371/journal.pone.0304771

A Sign Language Recognition System for Helping Disabled People

Published in 2023 5th International Conference on Sustainable Technologies for Industry 5.0 (STI), 2023

This is deep learning based sign language recognition system which convert the sign to text data for helping disabled people

Recommended citation: H. Adhikari, M. S. Bin Jahangir, I. Jahan, M. S. Mia and M. R. Hassan, "A Sign Language Recognition System for Helping Disabled People," 2023 5th International Conference on Sustainable Technologies for Industry 5.0 (STI), Dhaka, Bangladesh, 2023, pp. 1-6, doi: 10.1109/STI59863.2023.10465011. https://ieeexplore.ieee.org/abstract/document/10465011

Eye Tracking, Saliency Modeling and Human Feedback Descriptor Driven Robust Region-of-Interest Determination Technique

Published in IEEE Access, 2022

Region of Interest (ROI) determination process using subjective methods (e.g. using human vision) differ from the objective ones (e.g. using mathematical modelling). A novel method is proposed to determine ROI by combining subjective and objective information. Compared to existing deep learning based (MxSalNet) and depth pixel (DP) based ROI, the selection of ROI using the proposed method is more realistic

Recommended citation: M. Paul, P. K. Podder and M. R. Hassan, "Eye Tracking, Saliency Modeling and Human Feedback Descriptor Driven Robust Region-of-Interest Determination Technique," in IEEE Access, vol. 10, pp. 98612-98624, 2022, doi: 10.1109/ACCESS.2022.3206045. https://ieeexplore.ieee.org/document/9887942

An IoT Based Water Quality Testing Device: An Approach to Modelling a Geographical Map Based on Water Quality Data and Decision Support System

Published in International Journal of Advanced Trends in Computer Science and Engineering, 2021

Normally water quality is evaluated in a scientific laboratory which is expensive, time-consuming and impractical for real-time implementation. Thus, researchers have introduced different electronic devices to test water quality in real-time. However, these methods focused only on testing device development rather than disseminating resultant information for real-life applications. Moreover, these devices did not have any correlation with mobile decision support system and notification. To address these limitations, an integrated decision support system has been developed for instantaneous water quality detection by hardware configuration, smartphone application and web-based mapping.

Recommended citation: M. R. Hassan, P. K. Podder, T. Debnath, and M. O. Faruk “An IoT Based Water Quality Testing Device: An Approach to Modelling a Geographical Map Based on Water Quality Data and Decision Support System”, International Journal of Advanced Trends in Computer Science and Engineering, Volume 10, No.2, Pages: 1091-1099, April 2021 (ISSN 2278-309) https://doi.org/10.30534/ijatcse/2021/851022021