![Prosper Oyibo](https://profiles-cardiff.imgix.net/attachments/5292?w=200&h=200&auto=format&crop=faces&fit=crop&q=90")
Overview
My academic journey has been primarily focused on the development of automated diagnostics for poverty-related parasitic diseases during my doctoral studies at Delft University of Technology. I possess a rich experience in multidisciplinary research, collaborating with stakeholders from diverse sectors, including NGOs, medicine, parasitology, public health, government, and engineering. Through these collaborations, I have successfully delivered research with high-impact societal outcomes.
Currently, my research is centered on the development and validation of multi-modality quantitative medical image analysis software for cancer diagnosis. This work represents a natural progression of my expertise, combining my background in automated diagnostics with cutting-edge advancements in medical imaging technology.
Publication
2025
- Oyibo, P. et al. 2025. 3D DeepLab-based automated GTV segmentation in head and neck cancer using PET/CT imaging. Presented at: ESTRO 2025 Vienna, Austria 2 - 6 May 2025. Radiotherapy and Oncology. Vol. 206.Elsevier. , pp.S2536-S2538. (10.1016/S0167-8140(25)01892-4)
- Oyibo, P. et al. 2025. Deep learning-based automated detection and multiclass classification of soil-transmitted helminths and Schistosoma mansoni eggs in fecal smear images. Scientific Reports 15 (1) 21495. (10.1038/s41598-025-02755-9)
2024
- Meulah, B. et al., 2024. Validation of artificial intelligence-based digital microscopy for automated detection of Schistosoma haematobium eggs in urine in Gabon. PLoS Neglected Tropical Diseases 18 (2) e0011967. (10.1371/journal.pntd.0011967)
- Oyibo, P. et al. 2024. An automated slide scanning system for membrane filter imaging in diagnosis of urogenital schistosomiasis. Journal of Microscopy 294 (1), pp.52-61. (10.1111/jmi.13269)
- Oyibo, P. , Brynolfsson, P. and Spezi, E. 2024. Integrating radiomic image analysis in the Hero Imaging platform. Presented at: Cardiff University School of Engineering Research Conference 2024 Cardiff, UK 12th - 14th June 2024. Published in: Spezi, E. and Bray, M. eds. Proceedings of the Cardiff University School of Engineering Research Conference 2024. Cardiff University Press. , pp.23-27. (10.18573/conf3.g)
2023
- Oyibo, P. et al. 2023. Two-stage automated diagnosis framework for urogenital schistosomiasis in microscopy images from low-resource settings. Journal of Medical Imaging 10 (04) 044005. (10.1117/1.JMI.10.4.044005)
2022
- Bengtson, M. et al., 2022. A usability study of an innovative optical device for the diagnosis of schistosomiasis in Nigeria. Presented at: Global Humanitarian Technology Conference (GHTC) Santa Clara, CA, USA 8-11 September 2022. Proceedings of Global Humanitarian Technology Conference. IEEE. , pp.17-22. (10.1109/GHTC55712.2022.9911019)
- Meulah, B. et al., 2022. Performance evaluation of the Schistoscope 5.0 for (semi-)automated digital detection and quantification of schistosoma haematobium eggs in Urine: A field-based study in Nigeria. American Journal of Tropical Medicine and Hygiene 107 (5), pp.1047–1054. (10.4269/ajtmh.22-0276)
- Oyibo, P. et al. 2022. Schistoscope: An automated microscope with artificial intelligence for detection of schistosoma haematobium eggs in resource-limited settings. Micromachines 13 (5) 643. (10.3390/mi13050643)
2021
- Carel Diehl, J. et al., 2021. Schistoscope: smartphone versus Raspberry Pi based low-cost diagnostic device for urinary Schistosomiasis. Presented at: 2020 IEEE Global Humanitarian Technology Conference (GHTC) Seattle 29 October - 1 November 2020. Proceedings of 2020 IEEE Global Humanitarian Technology Conference (GHTC). IEEE. (10.1109/ghtc46280.2020.9342871)
Articles
- Meulah, B. et al., 2022. Performance evaluation of the Schistoscope 5.0 for (semi-)automated digital detection and quantification of schistosoma haematobium eggs in Urine: A field-based study in Nigeria. American Journal of Tropical Medicine and Hygiene 107 (5), pp.1047–1054. (10.4269/ajtmh.22-0276)
- Meulah, B. et al., 2024. Validation of artificial intelligence-based digital microscopy for automated detection of Schistosoma haematobium eggs in urine in Gabon. PLoS Neglected Tropical Diseases 18 (2) e0011967. (10.1371/journal.pntd.0011967)
- Oyibo, P. et al. 2024. An automated slide scanning system for membrane filter imaging in diagnosis of urogenital schistosomiasis. Journal of Microscopy 294 (1), pp.52-61. (10.1111/jmi.13269)
- Oyibo, P. et al. 2022. Schistoscope: An automated microscope with artificial intelligence for detection of schistosoma haematobium eggs in resource-limited settings. Micromachines 13 (5) 643. (10.3390/mi13050643)
- Oyibo, P. et al. 2025. Deep learning-based automated detection and multiclass classification of soil-transmitted helminths and Schistosoma mansoni eggs in fecal smear images. Scientific Reports 15 (1) 21495. (10.1038/s41598-025-02755-9)
- Oyibo, P. et al. 2023. Two-stage automated diagnosis framework for urogenital schistosomiasis in microscopy images from low-resource settings. Journal of Medical Imaging 10 (04) 044005. (10.1117/1.JMI.10.4.044005)
Conferences
- Bengtson, M. et al., 2022. A usability study of an innovative optical device for the diagnosis of schistosomiasis in Nigeria. Presented at: Global Humanitarian Technology Conference (GHTC) Santa Clara, CA, USA 8-11 September 2022. Proceedings of Global Humanitarian Technology Conference. IEEE. , pp.17-22. (10.1109/GHTC55712.2022.9911019)
- Carel Diehl, J. et al., 2021. Schistoscope: smartphone versus Raspberry Pi based low-cost diagnostic device for urinary Schistosomiasis. Presented at: 2020 IEEE Global Humanitarian Technology Conference (GHTC) Seattle 29 October - 1 November 2020. Proceedings of 2020 IEEE Global Humanitarian Technology Conference (GHTC). IEEE. (10.1109/ghtc46280.2020.9342871)
- Oyibo, P. , Brynolfsson, P. and Spezi, E. 2024. Integrating radiomic image analysis in the Hero Imaging platform. Presented at: Cardiff University School of Engineering Research Conference 2024 Cardiff, UK 12th - 14th June 2024. Published in: Spezi, E. and Bray, M. eds. Proceedings of the Cardiff University School of Engineering Research Conference 2024. Cardiff University Press. , pp.23-27. (10.18573/conf3.g)
- Oyibo, P. et al. 2025. 3D DeepLab-based automated GTV segmentation in head and neck cancer using PET/CT imaging. Presented at: ESTRO 2025 Vienna, Austria 2 - 6 May 2025. Radiotherapy and Oncology. Vol. 206.Elsevier. , pp.S2536-S2538. (10.1016/S0167-8140(25)01892-4)
Biography
- 2019-2025: PhD in System and Control Engineering, Delft University of Technology, Netherlands. Title : Development of smart optical diagnostic device for parasitic diseases (Supervisors: Prof. Gleb Vdovine, Prof. Jan-Carel Diehl, Prof. Wellington Oyibo).
- 2014-2017: Masters of Control Engineering, Ahmadu Bello University, Zaria, Nigeria. Title : Development a power-line detection algorithm for optical images (Supervisors: Prof. M. B. Mu'azu, Prof. Boyi Jimoh).
Contact Details
[email protected]
Queen's Buildings - North Building, Room N1.51, 5 The Parade, Newport Road, Cardiff, CF24 3AA
Queen's Buildings - North Building, Room N1.51, 5 The Parade, Newport Road, Cardiff, CF24 3AA
Specialisms
- Computer vision
- Machine learning
- Digital Microscopy
- medical image analysis
- Control engineering