![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., Meulah, B., Agbana, T., van Lieshout, L., Oyibo, W., Vdovine, G. and Diehl, J. C. 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), article number: 21495. (10.1038/s41598-025-02755-9)
- 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 2025Radiotherapy and Oncology, Vol. 206. Elsevier pp. S2536-S2538., (10.1016/S0167-8140(25)01892-4)
2024
- 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 Presented at 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., Agbana, T., van Lieshout, L., Oyibo, W., Diehl, J. and Vdovine, G. 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)
- 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), article number: e0011967. (10.1371/journal.pntd.0011967)
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(4), article number: 44005. (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 2022Proceedings of Global Humanitarian Technology Conference. IEEE pp. 17-22., (10.1109/GHTC55712.2022.9911019)
- 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), article number: 643. (10.3390/mi13050643)
- 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)
2021
- Carel Diehl, J., Oyibo, P., Agbana, T., Jujjavarapu, S., Van, G., Vdovin, G. and Oyibo, W. 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 2020Proceedings of 2020 IEEE Global Humanitarian Technology Conference (GHTC). IEEE, (10.1109/ghtc46280.2020.9342871)
Articles
- Oyibo, P., Meulah, B., Agbana, T., van Lieshout, L., Oyibo, W., Vdovine, G. and Diehl, J. C. 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), article number: 21495. (10.1038/s41598-025-02755-9)
- Oyibo, P., Agbana, T., van Lieshout, L., Oyibo, W., Diehl, J. and Vdovine, G. 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)
- 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), article number: e0011967. (10.1371/journal.pntd.0011967)
- 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(4), article number: 44005. (10.1117/1.JMI.10.4.044005)
- 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), article number: 643. (10.3390/mi13050643)
- 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)
Conferences
- 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 2025Radiotherapy and Oncology, Vol. 206. Elsevier pp. S2536-S2538., (10.1016/S0167-8140(25)01892-4)
- 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 Presented at 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)
- 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 2022Proceedings of Global Humanitarian Technology Conference. IEEE pp. 17-22., (10.1109/GHTC55712.2022.9911019)
- Carel Diehl, J., Oyibo, P., Agbana, T., Jujjavarapu, S., Van, G., Vdovin, G. and Oyibo, W. 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 2020Proceedings of 2020 IEEE Global Humanitarian Technology Conference (GHTC). IEEE, (10.1109/ghtc46280.2020.9342871)
Biography
- 2019-2024: 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
OyiboP@cardiff.ac.uk
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