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BSc (Beijing), MSc, MPhil (Essex)

Hongbo Du holds a BSc in Computer Automation (Beijing), an MSc and an MPhil in Computing (Essex). Over his tenure at The University of �鶹ֱ��, Hongbo has served in various roles including module leads, programme directors, member of Senate and Council.

Prof Du’s primary areas of research are in Data Mining and Machine Learning, Image Processing and Time Series Analysis. He is broadly interested in applying machine learning techniques in solving practical problems. His recent work involves analysing ultrasound images to diagnose various types of cancer and analysing biomedical images for tissue anomalies. He also worked on data stream clustering and time series pattern discovery. His other interests include real-time complex event processing. Prof Du has been involved in several collaborative projects with external partners including TenD Medical AI Technologies, DeepNet Security, Wellcome Trust Sanger Institute, KU Leuven, Vitalograph and Flintec. He has co-authored many peer-reviewed journal and conference papers and supervised several PhD theses and MRes dissertations in medical image analysis and understanding. He is currently supervising PhD and MSc projects in medical image and time series analysis.

At present, Hongbo is the module lead for Principles of Database Systems (level 5) and Applied Techniques of Data Mining and Machine Learning (level 7). He has extensive experience in delivering various modules and plays a significant role in the development of the computing curriculum. He is currently the director for GPP collaboration programmes.

Tel: +44 (0)1280 828298 / 828322

Select publications

• F. Bianconi, MU Khan, H. Du, S. Jassim, “Experimental Assessment of Conventional Features, CNN-Based Features and Ensemble Schemes for Discriminating Benign Versus Malignant Lesions on Breast Ultrasound Images”. Ultrasonic Imaging. 2025;0(0).
• M. U. Khan, F. Bianconi, H. Du and S. Jassim, “Hand-crafted Vs. deep CNN features to distinguish benign from malignant lesions in breast ultrasound images,” 2025 International Conference on Control, Automation and Diagnosis (ICCAD), Barcelona, Spain, 2025, pp. 1 – 6, DOI:
• Z. Q. Yang, Y Zhang, F Lu, T. Yang, J. Shan, Q. Jiang, G. H. Lim, F. Bertucci, H. Du, Y. C. Zhu, “Integrating multimodal ultrasound imaging for improved radiomics sentinel lymph node assessment in breast cancer”, Gland Surgery, 2025 Jul 31;14(7):1348-1365. DOI:
• P. Obiorah, G. Diri, and H. Du, “Comparative evaluation of traditional, lexicon-based, and transformer models for sentiment classification”, In M. Jones (Ed.), Proceedings of InSITE 2025: Informing Science and Information Technology Education Conference, 20 – 28 July 2025, Article 23. Informing Science Institute. doi:10.28945/5490
• AlZoubi, A.; Eskandari, A.; Yu, H.; Du, H. Explainable DCNN Decision Framework for Breast Lesion Classification from Ultrasound Images Based on Cancer Characteristics. Bioengineering 2024, 11, 453. DOI
• Yu, H.; AlZoubi, A.; Zhao, Y.; Du, H. “Machine Learning Technology in Biomedical Engineering”, Special Issue, Bioengineering, April 2024
• M. Ahmed, H. Du and A. AlZoubi. “ENAS-B: Combining ENAS With Bayesian Optimization for Automatic Design of Optimal CNN Architectures for Breast Lesion Classification From Ultrasound Images”, Ultrasound Imaging, 2023, DOI:
• Han D, Ibrahim N, Lu F, Zhu Y, Du H, AlZoubi A. Automatic Detection of Thyroid Nodule Characteristics From 2D Ultrasound Images. Ultrasonic Imaging. 2023;0(0). doi:10.1177/01617346231200804
• AlZoubi, A., Lu, F., Zhu, Y. Ying, T. Ahmed, M. and Du, H. “Classification of breast lesions in ultrasound images using deep convolutional neural networks: transfer learning versus automatic architecture design”, Medical & Biological Engineering and Computing (2023).
• T Hassan, A AlZoubi, H Du, S Jassim. “Ultrasound image augmentation by tumor margin appending for robust deep learning based breast lesion classification”, Multimodal Image Exploitation and Learning 2022 12100, 80-89, 2022
• F Mohammad, A AlZoubi, H Du, S Jassim. “Machine leaning assessment of border irregularity of thyroid nodules from ultrasound images”, Multimodal Image Exploitation and Learning 2022 12100, 50-64, 2022
• S Zhang, A AlZoubi, H Du. “Fully convolutional network for breast lesion segmentation in ultrasound image: towards false positive reduction”, Multimodal Image Exploitation and Learning 2022 12100, 65-79, 2022
• A Eskandari, H Du, A AlZoubi. “Clustered-CAM: Visual Explanations for Deep Convolutional Networks for Thyroid Nodule Ultrasound Image Classification”, Medical Imaging with Deep Learning 2022
• F Mohammad, A AlZoubi, H Du, S Jassim. “Irregularity Recognition of Tumor Border in Ultrasound Thyroid Scans Without Segmentation”, Annual Conference on Medical Image Understanding and Analysis (MIUA2022), 110-113, 2022
• Zhu, Y-C., Du, H. Jiang, Q., Zhang, T., Huang, X-J., Zhang, Y., Shi, X-R., Shan, J. and AlZoubi, A., “Machine Learning Assisted Doppler Features for Enhancing Thyroid Cancer Diagnosis: A Multi-Cohort Study”, Journal of Ultrasound in Medicine, November 2021, DOI:
• Bose A., Nguyen T., Du H., AlZoubi A. (2022) Faster RCNN Hyperparameter Selection for Breast Lesion Detection in 2D Ultrasound Images. In: Jansen T., Jensen R., Mac Parthaláin N., Lin CM. (eds) Advances in Computational Intelligence Systems. UKCI 2021. Advances in Intelligent Systems and Computing, vol 1409. Springer, Cham.
• Eskandari A., Du H., AlZoubi A. (2021) Towards Linking CNN Decisions with Cancer Signs for Breast Lesion Classification from Ultrasound Images. In: Papież B.W., Yaqub M., Jiao J., Namburete A.I.L., Noble J.A. (eds) Medical Image Understanding and Analysis. MIUA 2021. Lecture Notes in Computer Science, vol 12722. Springer, Cham., pp423 – 437
• Ahmed M., AlZoubi A., Du H. (2021) Improving Generalization of ENAS-Based CNN Models for Breast Lesion Classification from Ultrasound Images. In: Papież B.W., Yaqub M., Jiao J., Namburete A.I.L., Noble J.A. (eds) Medical Image Understanding and Analysis. MIUA 2021. Lecture Notes in Computer Science, vol 12722. Springer, Cham., pp438-453,
• Jehan Ghafuri, Hongbo Du, Sabah Jassim, “Sensitivity and stability of pretrained CNN filters,” Proc. SPIE 11734, Multimodal Image Exploitation and Learning 2021, 117340B (12 April 2021); doi: 10.1117/12.2589521
• Tahir Hassan, Alaa AlZoubi, Hongbo Du, Sabah Jassim, “Towards optimal cropping: breast and liver tumor classification using ultrasound images,” Proc. SPIE 11734, Multimodal Image Exploitation and Learning 2021, 117340G (12 April 2021); doi: 10.1117/12.2589038
• Dhurgham Al-Karawi, Dheyaa Ibrahim, Hisham Al-Assam, Hongbo Du, and Sabah Jassim “A model-based adaptive method for speckle noise reduction in ultrasound images of ovarian tumours: a new approach”, Proc. SPIE 11734, Multimodal Image Exploitation and Learning 2021, 117340J (12 April 2021);
• D. Al-Karawi, H. Al-Assam, H.Du, A. Sayasneh, C. Landolfo, D. Timmerman, T. Bourne, S. Jassim, “An Evaluation of the Effectiveness of Image-based Texture Features Extracted from Static B-mode Ultrasound Images in Distinguishing between Benign and Malignant Ovarian Masses”, SAGE Ultrasonic Imaging journal, 25, Feb. 2021, DOI: 10.1177/0161734621998091
• Y. Zhu, A. AlZoubi, S. Jassim, Q. Jiang, Y. Zhang, Y. Wang, X. Ye, H. Du, “A generic deep learning framework to classify thyroid and breast lesions in ultrasound images”, Ultrasonics, Vol.110, (February, 2021), DOI:
• M Ahmed, H. Du, and A. Al Zoubi, “An ENAS Based Approach for Constructing Deep Learning Models for Breast Cancer Recognition from Ultrasound Images”, International Conference on Medical Image with Deep Learning (MIDL2020), Montréal, 6-8 July 2020
• F. Mohammad, A. Al Zoubi, H. Du, and S. Jassim, “Automatic Glass Crack Recognition for High Building Façade Inspection”, Proc. SPIE 11399, Mobile Multimedia/Image Processing, Security, and Applications 2020, 113990W (19 May 2020), doi: 10.1117/12.2567409
• J. Ghafuri, H. Du, and S. Jassim, “Topological aspects of CNN convolution layers for medical image analysis”, Proc. SPIE 11399, Mobile Multimedia/Image Processing, Security, and Applications 2020, 113990X (19 May 2020), doi: 10.1117/12.2567476
• José Martínez-Más,  Andrés Bueno-Crespo, Shan Khazendar , Manuel Remezal-Solano , Juan-Pedro Martínez-Cendán , Sabah Jassim , Hongbo Du , Hisham Al Assam Tom Bourne , Dirk Timmerman,  “Evaluation of machine learning methods with Fourier Transform features for classifying ovarian tumors based on ultrasound images”, PLOS ONE, 26 July 2019, DOI: 10.1371/journal.pone.0219388
• Al-Karawi, D., Landolfo, C., Du, H., Al-Assam, H., Sayaneh, A., Timmerman, D., Bourne, T. and Jassim, S., “Prospective clinical evaluation of texture‐based features analysis of ultrasound ovarian scans for distinguishing benign and malignant adnexal tumors”, Australian Journal of Ultrasound in Medicine, Vol.22, No.2, May 2019, p144
• A. A. A. Alazeez, S. Jassim and H. Du, “SLDPC: Towards Second Order Learning for Detecting Persistent Clusters in Data Streams,” 2018 10th Computer Science and Electronic Engineering (CEEC), Colchester, United Kingdom, 2018, pp. 248-253.
• A.Al Abd Alazeez, S. Jassim and H. Du, “TPICDS: A Two-Phase Parallel Approach for Incremental Clustering of Data Streams”, Euro-Par 2018 International Workshops, Turin, Italy, August 27-28, 2018, Revised Selected Papers, Lecture Notes in Computer Science by Springer International Publishing, Vol. 11339, No.1, January 2019, pp5-16, DOI: 10.1007/978-3-030-10549-5
• D. Han, H. Du and S. Jassim, “Controlling Sensitivity of Gaussian Bayes Predictions based on Eigenvalue Thresholding”, EAI Transactions on Industrial Networks and Intelligent Systems, 5(16), November 2018, DOI: 10.4108/eai.29-11-2018.155885
• A. Al Abd Alazeez, S. Jassim and H. Du, “EDDS: An Enhanced Density-based Method for Clustering Data Streams”, Proceedings of 46th International Conference on Parallel Processing Workshops, University of Bristol, August 2017, DOI 10.1109/ICPPW.2017.27, pp103-112
• D. Ibrahim, H. Al-Assam, S. Jassim & H. Du, “Multi-level Trainable Segmentation for Measuring Gestational and Yolk Sacs from Ultrasound Images“, MIUA 2017: Medical Image Understanding and Analysis (July 2017, CCIS Series vol. 723), 86-97
• O. Al-Okashi, H. Al-Assam & H. Du, “Automatic pelvis segmentation from x-ray images of a mouse model”, Proc. SPIE, Mobile Multimedia/Image Processing, Security, and Applications (May 2017), pp.1022108-1022108-5
• D. Ibrahim, H. Al-Assam, H. Du & S. Jassim, “Trainable segmentation of multilocular cysts based on local basic pixel features”, Proc. SPIE, Mobile Multimedia/Image Processing, Security, and Applications (May 2017), pp.102210B-102210B-8
• D. Al-Karawi, A. Sayasneh, H. Al-Assam, S. Jassim, N. Page, D. Timmerman, T. Bourne & H. Du, “Automated differentiation of ovarian mature teratomas from other benign tumours using neural networks classification of 2D ultrasound static images”, Proc. SPIE, Mobile Multimedia/Image Processing, Security, and Applications (May 2017), pp.102210F-102210F-10
• O. Al Okashi, H. Du & H. Al-Assam, “Automatic spine curvature estimation from X-ray images of a mouse model”, Journal of Computer Methods and Programs in Biomedicine 140 (March 2017), 175–184
• A. Alazeez, S. Jassim & H. Du, “EINCKM: An Enhanced Prototype-based Method for Clustering Evolving Data Streams in Big Data”, Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods (ICPRAM 2017) (Porto, February 2017), 173-183
• D. Han, H. Du & S. Jassim, “Towards a Confidence-Centric Classification Based on Gaussian Models and Bayesian Principles”, Proceedings of 9th York Doctoral Symposium on Computer Science and Electronics (University of York, November 2016), 46-56
• D. Ahmed Ibrahim, H. Al-Assam, H. Du, D. Al-karawi, S. Jassim et al., “Automatic segmentation and measurements of gestational sac using static B-mode ultrasound images”, Proc. SPIE 9869, Mobile Multimedia/Image Processing, Security, and Applications 2016
• D. Han, N. Al Jawad & H. Du, “Facial expression identification using 3D geometric features from Microsoft Kinect device”, Proc. SPIE 9869, Mobile Multimedia/Image Processing, Security, and Applications 2016
• S. Khazendar, A. Sayasneh, H. Al-Assam, H. Du, J. Kaijser, L. Ferrara, D. Timmerman, S. Jassim & T. Bourne, “Automated characterisation of ultrasound images of ovarian tumours: the diagnostic accuracy of a support vector machine and image processing with a local binary pattern operator”, Facts, Views and Visions in ObGyn 7.1 (March 2015), 7-15
• Al-Karawi, D., Landolfo, C., Du, H., Al-Assam, H., Sayaneh, A., Timmerman, D., Bourne, T. and Jassim, S., “Prospective clinical evaluation of texture‐based features analysis of ultrasound ovarian scans for distinguishing benign and malignant adnexal tumors”, Australian Journal of Ultrasound in Medicine, Vol.22, No.2, May 2019, p144