Dr Claymond Lim Wei Xiang

Dr Claymond Lim Wei Xiang

  • Lecturer
Department of Data Science and Artificial Intelligence
  • School of Computing and Artificial Intelligence
Faculty of Engineering and Technology
SDGs Focus

Biography

Dr. Claymond Lim holds a PhD in Computer Science from the University of Nottingham (Malaysia Campus), specializing in deep learning, medical image analysis, and explainable AI (XAI). His doctoral research advanced the detection of diabetic retinopathy—a leading cause of vision loss among people with diabetes—by developing AI models that are both accurate and transparent, ensuring medical decisions can be trusted by clinicians and patients alike.

Beyond academia, he has applied his expertise in industry as a Cloud Engineer at Zetrix AI and as a Cloud Solution Architect at Huawei Technologies (Malaysia) Sdn. Bhd. At Huawei, he represented Huawei Cloud Malaysia as a panellist at the 54th Executive Board Meeting of the Organization of Asia-Pacific News Agencies (OANA), hosted by BERNAMA, contributing to discussions on how AI can revolutionize news production workflows and enhance operational efficiency.

At ºìÐÓÊÓƵ University, he is committed to advancing AI for healthcare, driving innovations that improve patient outcomes while fostering transparency, accountability, and societal trust in AI-driven systems.

Academic & Professional Qualifications

  • PhD in Computer Science - Artificial Intelligence, University of Nottingham Malaysia (2024)
  • MSc. in Software Engineering - Asia Pacific University (2018)
  • BSc in Computer Science - ºìÐÓÊÓƵ University (2016)

Research Interests

  • Deep Learning
  • Medical Image Analysis
  • Explainable AI

Notable Publications

  1. Lim, W.X., Chen, Z. and Ahmed, A., 2022. The adoption of deep learning interpretability techniques on diabetic retinopathy analysis: a review. Medical & biological engineering & computing, 60(3), pp.633-642.
  2. Lim, W.X. and Chen, Z., 2024. Enhancing deep learning pre-trained networks on diabetic retinopathy fundus photographs with SLIC-G. Medical & Biological Engineering & Computing, 62(8), pp.2571-2583.
  3. Lim, W.X., Chen, Z. and Ahmed A., 2021. Quantitative Interpretable Convolutional Neural Network to Diagnose Diabetic Retinopathy, SPRINGER NATURE – Research Book Series: Transactions on Computational Science & Computational Intelligence. (In Press.)
  4. Lim, W.X., Chen, Z., Ahmed, A., Chandesa, T. and Liao, I., 2020. A review of machine learning techniques for applied eye fundus and tongue digital image processing with diabetes management system. arXiv preprint arXiv:2012.15025.