Faculty of Computing, Law and Psychology | School of Computing

Dr Alaa AlZoubi

Visiting Lecturer

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University of Buckingham - Alaa AlzoubiDr. Alaa AlZoubi is a Research Fellow in Machine Learning and Computer Vision at The University of Buckingham. He holds a BSc in Computer Information System, MSc Computer Information System, and a PhD in Computer Science from the University of Lincoln in 2017. Prior to join the School of Computing, Alaa was a Research Fellow in Computer Vision at the Centre for Electronic Warfare Information and Cyber, Cranfield University, Defence Academy of the United Kingdom.

Dr. AlZoubi specialises in Computer Vision, Machine Learning, Deep Learning, Medical Image Analysis, Time Series Analysis, Autonomous Systems, and Geographic Information System (GIS). He has more than 8 years of industrial experience, in Computer Vision R&D, and GIS as a Senior Software Engineer.

If you are passionate about doing master by research or PhD in computer vision, deep learning, medical image analysis or autonomous systems areas and have excellent programming skills, please feel free to contact me.

Recently, Alaa won a research grant funded by Science and Technology Facilities Council (STFC) to investigate the development of robotics and remote sensing technologies to help with safer land mine surveying.

Selected Publications

  • Eskandari, Ali, Hongbo Du, and Alaa AlZoubi. “Clustered-CAM: Visual Explanations for Deep Convolutional Networks for Thyroid Nodule Ultrasound Image Classification.” In Medical Imaging with Deep Learning. 2022.
  • Zhang, Sicong, Alaa AlZoubi, and Hongbo Du. “Fully convolutional network for breast lesion segmentation in ultrasound image: towards false positive reduction.” In Multimodal Image Exploitation and Learning 2022, vol. 12100, pp. 65-79. SPIE, 2022.
  • Hassan, Tahir, Alaa AlZoubi, Hongbo Du, and Sabah Jassim. “Ultrasound image augmentation by tumor margin appending for robust deep learning based breast lesion classification.” In Multimodal Image Exploitation and Learning 2022, vol. 12100, pp. 80-89. SPIE, 2022.
  •  Mohammad, Fakher, Alaa AlZoubi, Hongbo Du, and Sabah Jassim. “Machine leaning assessment of border irregularity of thyroid nodules from ultrasound images.” In Multimodal Image Exploitation and Learning 2022, vol. 12100, pp. 50-64. SPIE, 2022.
  • Rahulan Radhakrishnan and Alaa AlZoubi. “Vehicle Pair-Activity Classification Using QTC and Long-Short Term Memory Neural Network.” In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2022) – Volume 5: VISAPP, ISBN 978-989-758-555-5, ISSN 2184-4321, pages 236-247.
  • Zhu, Yi‐Cheng, Hongbo Du, Quan Jiang, Tao Zhang, Xu‐Juan Huang, Yuan Zhang, Xiu‐Rong Shi, Jun Shan, and Alaa AlZoubi. “Machine Learning Assisted Doppler Features for Enhancing Thyroid Cancer Diagnosis: A Multi‐Cohort Study.” Journal of Ultrasound in Medicine (2021).
  • Anu Bose, Tuan Nguyen, Hongbo Du, and Alaa AlZoubi. “Faster RCNN Hyperparameter Selection for Breast Lesion Detection in 2D Ultrasound Images.” In UK Workshop on Computational Intelligence, pp. 179-190. Springer, Cham, 2021.
  • Alaa AlZoubi, Zhu, Yi-Cheng, Sabah Jassim, Quan Jiang, Yuan Zhang, Yong-Bing Wang, Xian-De Ye, and D. U. Hongbo. “A generic deep learning framework to classify thyroid and breast lesions in ultrasound images.” Ultrasonics 110 (2021): 106300.
  • Eskandari, Ali, Hongbo Du, and Alaa AlZoubi. “Towards Linking CNN Decisions with Cancer Signs for Breast Lesion Classification from Ultrasound Images.” In Annual Conference on Medical Image Understanding and Analysis, pp. 423-437. Springer, Cham, 2021.
  • Ahmed, Mohammed, Alaa AlZoubi, and Hongbo Du. “Improving Generalization of ENAS-Based CNN Models for Breast Lesion Classification from Ultrasound Images.” In Annual Conference on Medical Image Understanding and Analysis, pp. 438-453. Springer, Cham, 2021.
  • Hassan, Tahir, Alaa AlZoubi, Hongbo Du, and Sabah Jassim. “Towards optimal cropping: breast and liver tumor classification using ultrasound images.” In Multimodal Image Exploitation and Learning 2021, vol. 11734, p. 117340G. International Society for Optics and Photonics, 2021.
  • Ibrahim, Nasiru, Shathel Fahs, and Alaa AlZoubi. “Land cover analysis using satellite imagery for humanitarian mine action and ERW survey.” In Multimodal Image Exploitation and Learning 2021, vol. 11734, p. 1173402. International Society for Optics and Photonics, 2021.
  • Mohammad, F., A. AlZoubi, H. Du, and S. Jassim. “A generic approach for automatic crack recognition in buildings glass facade and concrete structures.” In Thirteenth International Conference on Digital Image Processing (ICDIP 2021), vol. 11878, p. 1187808. International Society for Optics and Photonics, 2021.
  • Mohammad, Fakher, Alaa AlZoubi, Hongbo Du, and Sabah Jassim. “Automatic glass crack recognition for high building façade inspection.” In Mobile Multimedia/Image Processing, Security, and Applications 2020, vol. 11399, p. 113990W. International Society for Optics and Photonics, 2020.
  • Ahmed, M., Du, H., & AlZoubi, A. (2020). “An ENAS Based Approach for Constructing Deep Learning Models for Breast Cancer Recognition from Ultrasound Images.” Medical Imaging with Deep Learning (MIDL) conference, 2020.
  • AlZoubi A., Nam D. “Vehicle Activity Recognition Using DCNN”. Communications in Computer and Information Science, vol 1182. Springer. (2020).
  • A.AlZoubi and D.Nam “Vehicle Activity Recognition Using Mapped QTC Trajectories.” International Conference on Computer Vision Theory and Applications – VISAPP. (2019).
  • A.Grenier, A.AlZoubi, L.Feetham and D.Nam “Towards Scene Understanding Implementing the Stixel World.” IEEE British and Irish Conference on Optics and Photonics – BICOP. (2018).
  • John Fardoulis, Steven Kay, A. AlZoubi , Nabil Aouf, and Ranah Irshad. “Robotics and Remote Sensing for Humanitarian Mine Action & ERW Survey (RRS-HMA)”.15th international symposium in mine action, 2018. 
  • A. AlZoubi, T. W. Pike, B. Al-Diri and P. Dickinson. “Pair-Activity Analysis from Video Using Qualitative Trajectory Calculus”. IEEE Transactions on Circuits and Systems for Video Technology, 2017.
  • A. AlZoubi, T. K. Kleinhappel, T. W. Pike, B. Al‐Diri and P. Dickinson. “Solving Orientation Duality for 3D Circular Features Using Monocular Vision“. International Conference on Computer Vision Theory and Applications – VISAPP (2015).
  • A. AlZoubi, P. Dickinson, T.W. Pike, T. K. Kleinhappel and B. Al-Diri. “Analysing Fish Behaviours Using Three-Dimensional Qualitative Trajectory Calculus“. The Ninth York Doctoral Symposium on Computer Science and Electronics (YDS, 2016).
  • A. AlZoubi, T. K. Kleinhappel,  T. W. Pike, B. Al‐Diri and P. Dickinson. “Reconciling Orientation Duality for 3D Circular Features Using Monocular Vision“. British Machine Vision Workshop – BMVC (2014).
  • A. Al‐Zoubi, B. Al‐Diri , T. K. Kleinhappel,  T. W. Pike and P. Dickinson. “Estimating 3D Orientation Of Fish Using Monocular Vision“. The British Machine Vision Association – BMVA (2014). (Poster).
  • T. Kleinhappel, A.AlZoubi, B. Al-Diri, O. Burman, P.Dickinson, L. John, A.Wilkinson, and T.Pike. “A method for the automated long-term monitoring of three-spined stickleback gasterosteusaculeatus shoal dynamics“. Journal of fish biology(2014), 84(4):1228–1233.
  • A. Al‐Zoubi, B. Al‐Diri , T. K. Kleinhappel,  T. W. Pike and P. Dickinson. “Monitoring three-spined stickleback fish using computer vision techniques”. ASAB Easter Conference 2013. (Poster).
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