Dr Dhevisha Sukumarran

Dr Dhevisha Sukumarran

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

Biography

Dr Dhevisha Sukumarran specialises in Biomedical Engineering and is a registered Engineer with the Board of Engineers (Electronic branch). She holds a Bachelor’s degree and a PhD in Biomedical Engineering, with a research focus on the application of Artificial Intelligence in medical diagnostic solutions. Her work encompasses medical imaging and computer vision, with research interests centred on artificial intelligence–driven approaches for medical solutions. She has authored four publications as first author during her postgraduate studies.

Academic & Professional Qualifications

  • PhD in Biomedical Engineering (2024)
  • Bachelor's of Biomedical Engineering (2020)

Research Interests

  • Artificial Intelligence
  • Medical imaging
  • image processing
  • Computer Vision

Teaching Areas

  • Artificial Intelligence

Courses Taught

  • Introduction to Artificial Intelligence

Notable Publications

  1. Sukumarran, D., Hasikin, K., Mohd Khairuddin, A. S., Ngui, R., Wan Sulaiman, W. Y., Vythilingam, I., & Divis, P. C. S. (2023). An
    automated malaria cells detection from thin blood smear images using deep learning. Tropical biomedicine, 40(2), 208-219.
    https://doi.org/10.47665/tb.40.2.013
  2. Sukumarran, D., Hasikin, K., Mohd Khairuddin, A. S., Ngui, R., Wan Sulaiman, W. Y., Vythilingam, I., & Divis, P. C. S. (2024). An
    optimised YOLOv4 deep learning model for efficient malarial cell detection in thin blood smear images. Parasites & vectors,
    17(1), 188. https://doi.org/10.1186/s13071-024-06215-7
  3. Sukumarran, D., Hasikin, K., Mohd Khairuddin, A. S., Ngui, R., Wan Sulaiman, W. Y., Vythilingam, I., & Divis, P. C. S. (2024).
    Machine and deep learning methods in identifying malaria through microscopic blood smear: A systematic review.
    Engineering Applications of Artificial Intelligence, 133, 108529.https://doi.org/10.1016/j.engappai.2024.108529
  4. D. Sukumarran et al., "Automated Identification of Malaria-Infected Cells and Classification of Human Malaria Parasites Using a Two-Stage Deep Learning Technique," in IEEE Access, vol. 12, pp. 135746-135763, 2024, doi: 10.1109/ACCESS.2024.3459411.