About

I am a Senior Lecturer in Computer Science at the College of Science and Engineering. I am also a Visiting Lecturer at the University of Buckingham and a Digital Theme Ambassador at Digital Theme UK-Ukraine Twinning Initiative. Prior to joining the University of Derby, I was a Research Fellow in Machine Learning and Computer Vision at The University of Buckingham and a Research Fellow in Computer Vision at Cranfield University. My main research interests focus on Artificial intelligence, AI Decision Explainability, Deep Learning and Computer Vision. 

If you possess a strong passion for pursuing a PhD in deep learning or explainable AI for applications in industry or healthcare and have good programming skills, I encourage you to reach out to me.

Teaching responsibilities

Module Leader

• Object Oriented Analysis, Design and Implementation (postgraduate)
• Enterprise Systems (undergraduate)

Project Supervisor

• Independent Scholarship (MSc Big Data Analytics and IT)
• Independent Studies

PhD Project Supervisor

• Mohmmad Ahmad (2018 - 2022) - Thesis: Automatic Convolutional Neural Network Architecture Search for Breast Lesion Classification from Ultrasound Images: An ENAS Bayesian Optimization Approach
• FAKHER MOHAMMAD (2018 - 2022) - Thesis: Machine Learning Models for Image Faulty-Related Abnormality Recognition in Different Modalities
• ANU BOSE (2019 - 2023) - Thesis: Breast Lesion Detection in Ultrasound Images using Deep Neural Networks: Clustering Based Approach for False Positive Reduction
• TAHIR HASSAN (2019 - 2023) - Project: Breast and Liver Tumor Classification using Ultrasound Images
• SICONG ZHANG (2019 - current) - Project: Breast Lesion Segmentation using Deep Learning Neural Networks
• Richard Tucker (2019 - current / part-time) Project: Is there a role for artificial intelligence in accelerating the learning of radiographers in detecting visual anomalies on a chest x-ray?
• Ayman Massoud (2021 - current / part-time) Project: Asset Management and Reliability

Masters by Research Project Supervisor

• Ali Eskandari (2022) - Thesis: Towards CNN Decision Understanding for Breast and Thyroid Lesions Classification from Ultrasound Images
• Yi-Cheng Zhu (2022) - Thesis: Colour Doppler Ultrasound Image Analysis for Thyroid Cancer Classification Using Machine Learning Techniques
• Rahulan Radhakrishnan (2021) - Thesis: Vehicle Pair-Activity Recognition Using Long Short-Term Memory Neural Network

Professional interests

I am a research member of the Data Science Research Centre (DSRC) supporting innovative solutions to local and national industries in the areas of data science and artificial intelligence. In particular, I am part of the Collaborative and Data-centric Engineering. This interdisciplinary group collaborates on the use of artificial intelligence in modelling engineering products and processes. I am also part of the Health and Wellbeing research group. 

Research interests

My main research interests focus on Deep Learning, AI Decision Explainability and Computer Vision, with an emphasis on but not limited to Engineering Inspection Applications, Medical Image Analysis, Behavioral Analysis, and Autonomous Systems.

Externally funded research projects

• Inspector Plus / Application number: 10079284 / Competition: NATEP Helping SMEs innovate in aerospace: Spring 2023. Innovate UK, 2023
• Deep Learning for Engineering Inspection/ Application number: 10080115 / Competition: Feasibility studies for Artificial Intelligence solutions. Innovate UK, 2023
• Automatic Cancer Detection. University of Buckingham, 2022
• Develop drones, robotics systems and remote sensing to help with safer landmine surveying. Global Challenge Research (GCRF), STFC, 2019

Membership of professional bodies

• European Society of Radiology
• Committee Member at the International Conference on Natural Language and Information Systems
• Technical Committee Member at the International Conference on Digital Image Processing (ICDIP)
• Cancer Diagnostic Ultrasound Imaging with Deep Learning (CDUDL) Workshop (Chair)

Qualifications

• PhD in Computer Science, University of Lincoln, UK

Recent conferences

Selected conference papers

• AlZoubi, A and Ibrahim, N., 2023. Land Cover Analysis Using Sentinel-2 For Humanitarian Mine Action And ERW Survey. Digital Theme UK-Ukraine Twinning Conference
• Radhakrishnan, R. and AlZoubi, A., 2022, February. Automatic Bi-LSTM Architecture Search Using Bayesian Optimisation for Vehicle Activity Recognition. In International Joint Conference on Computer Vision, Imaging and Computer Graphics (pp. 108-134). Cham: Springer Nature Switzerland
• Mohammad, F., AlZoubi, A., Du, H. and Jassim, S., 2022, May. 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
• Hassan, T., Al Zoubi, A., Du, H. and Jassim, S., 2022, May. 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
• Mohammad, F., AlZoubi, A., Du, H. and Jassim, S., 2022, May. 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
• Zhang, S., Al Zoubi, A. and Du, H., 2022, May. 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
• Eskandari, A., Du, H. and AlZoubi, A., 2022, April. Clustered-CAM: Visual Explanations for Deep Convolutional Networks for Thyroid Nodule Ultrasound Image Classification. In Medical Imaging with Deep Learning
• Radhakrishnan, R. and AlZoubi, A., 2022. Vehicle Pair Activity Classification using QTC and Long Short Term Memory Neural Network. In VISIGRAPP (5: VISAPP) (pp. 236-247)
• Bose, A., Nguyen, T., Du, H. and AlZoubi, A., 2022. Faster RCNN hyperparameter selection for breast lesion detection in 2D ultrasound images. In Advances in Computational Intelligence Systems: Contributions Presented at the 20th UK Workshop on Computational Intelligence, September 8-10, 2021, Aberystwyth, Wales, UK 20 (pp. 179-190). Springer International Publishing
• Mohammad, F., AlZoubi, A., Du, H. and Jassim, S., 2021, June. 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, pp. 52-61). SPIE
• Ibrahim, N., Fahs, S. and AlZoubi, A., 2021, April. Land cover analysis using satellite imagery for humanitarian mine action and ERW survey. In Multimodal Image Exploitation and Learning 2021 (Vol. 11734, p. 1173402). SPIE
• Hassan, T., AlZoubi, A., Du, H. and Jassim, S., 2021, April. Towards optimal cropping: breast and liver tumor classification using ultrasound images. In Multimodal Image Exploitation and Learning 2021 (Vol. 11734, pp. 111-122). SPIE
• Ahmed, M., AlZoubi, A. and Du, H., 2021. Improving Generalization of ENAS-Based CNN Models for Breast Lesion Classification from Ultrasound Images. In Medical Image Understanding and Analysis: 25th Annual Conference, MIUA 2021, Oxford, United Kingdom, July 12–14, 2021, Proceedings 25 (pp. 438-453). Springer International Publishing
• Eskandari, A., Du, H. and AlZoubi, A., 2021. Towards linking CNN decisions with cancer signs for breast lesion classification from ultrasound images. In Medical Image Understanding and Analysis: 25th Annual Conference, MIUA 2021, Oxford, United Kingdom, July 12–14, 2021, Proceedings 25 (pp. 423-437). Springer International Publishing
• Ahmed, M., Du, H. and AlZoubi, A., 2020. An ENAS based approach for constructing deep learning models for breast cancer recognition from ultrasound images. arXiv preprint arXiv:2005.13695
• Mohammad, F., AlZoubi, A., Du, H. and Jassim, S., 2020, May. Automatic glass crack recognition for high building façade inspection. In Mobile Multimedia/Image Processing, Security, and Applications 2020 (Vol. 11399, pp. 213-228). SPIE
• AlZoubi, A. and Nam, D., 2020. Vehicle activity recognition using dcnn. In Computer Vision, Imaging and Computer Graphics Theory and Applications: 14th International Joint Conference, VISIGRAPP 2019, Prague, Czech Republic, February 25–27, 2019, Revised Selected Papers 14 (pp. 566-588). Springer International Publishing

Experience in industry

I have more than 8 years of industrial experience in Computer Vision R&D, and GIS as a Senior Software Engineer.

In the media

Transforming Inspections Within Rail and Road

Recent publications

Selected journal papers

• AlZoubi, A., Lu, F., Zhu, Y., Ying, T., Ahmed, M. and Du, H., 2023. Classification of breast lesions in ultrasound images using deep convolutional neural networks: transfer learning versus automatic architecture design. Medical & Biological Engineering & Computing, pp.1-15. https://doi.org/10.1007/s11517-023-02922-y 
• Han, D., Ibrahim, N., Lu, F., Zhu, Y., Yu, M., Du, H. and AlZoubi, A., Automatic Detection of Thyroid Nodule Characteristics From 2D Ultrasound Images. Ultrasonic Imaging. 2023;0(0). https://doi.org/10.1177/01617346231200804
• Ahmed, M., Du, H. and AlZoubi, A., 2023. ENAS-B: Combining ENAS With Bayesian Optimization for Automatic Design of Optimal CNN Architectures for Breast Lesion Classification From Ultrasound Images. Ultrasonic Imaging, p.01617346231208709
• AlZoubi, A., Zhu, Y.C., Jassim, S., Jiang, Q., Zhang, Y., Wang, Y.B., Ye, X.D. and Hongbo, D.U., 2021. A generic deep learning framework to classify thyroid and breast lesions in ultrasound images. Ultrasonics, 110, p.106300. https://doi.org/10.1016/j.ultras.2020.106300
• Zhu, Y.C., Du, H., Jiang, Q., Zhang, T., Huang, X.J., Zhang, Y., Shi, X.R., Shan, J. and AlZoubi, A., 2022. Machine Learning Assisted Doppler Features for Enhancing Thyroid Cancer Diagnosis: A Multi‐Cohort Study. Journal of Ultrasound in Medicine, 41(8), pp.1961-1974. https://doi.org/10.1002/jum.15873
• AlZoubi, A., Al-Diri, B., Pike, T., Kleinhappel, T. and Dickinson, P., 2017. Pair-activity analysis from video using qualitative trajectory calculus. IEEE Transactions on Circuits and Systems for Video Technology, 28(8), pp.1850-1863. doi: 10.1109/TCSVT.2017.2701860