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25 June 2025 | Story Dr Nitha Ramnath | Photo Lunga Luthuli
Dr Omololu Aluko
Dr Omololu Aluko, Senior Lecturer in the Department of Biostatistics advances health research and collaboration during prestigious fellowship at Ghent University, Belgium.

Dr Omololu Aluko, Senior Lecturer in the Department of Biostatistics in the Faculty of Health Sciences at the University of the Free State (UFS), recently completed a prestigious short research stay at Ghent University in Belgium. The fellowship, hosted in April 2025, was awarded through the highly competitive Africa Platform of Ghent University Association (GAP) funding scheme, with additional support from the UFS International Office. 

A recognised expert in infectious disease modelling, particularly HIV/AIDS research in low-resource settings, Dr Aluko has devoted his academic career to using biostatistics to address pressing health challenges. His selection for this fellowship marks a significant achievement both for his individual research trajectory and for the broader ambitions of the Faculty of Health Sciences and the UFS.

 

Strategic steps towards international collaboration 

The opportunity for the fellowship was first announced in the UFS Digest Newsletter. Motivated by the potential for international collaboration, Dr Aluko began seeking a host at Ghent University whose interests aligned with his own. After several weeks of correspondence with various departments and researchers, a suitable academic collaborator agreed to host him. 

With a host confirmed, Dr Aluko submitted his application, which underwent a rigorous review and selection process. His proposal was shortlisted and ultimately approved. While Ghent University provided partial funding, supplementary financial support was secured through the UFS International Office. Dr Aluko credits the office’s assistance – especially the guidance of Mr Kagiso Ngake, Senior Officer: Partnerships – for helping him successfully secure the necessary resources. 

 

Advancing research in health data science  

During his time at Ghent University, Dr Aluko focused on the application of machine learning algorithms to address public health challenges – an increasingly important field within the Faculty of Health Sciences. His research demonstrated how advanced data analysis techniques can improve health outcomes and optimise treatment strategies, especially in resource-constrained settings. 

Beyond the immediate research achievements, the fellowship laid a foundation for long-term collaboration between the UFS and Ghent University. Key outcomes include: 

  • Opportunities for joint PhD supervision, allowing UFS students to conduct part of their research at Ghent University 
  • Prospects for publishing collaborative research in leading international A1-rated journals 
  • The identification of a promising young research collaborator, paving the way for future academic partnerships 
  • Plans to explore future staff exchange programmes, as new funding calls are announced 

     

A growing partnership in a new academic field 

Dr Annelies Verdoolaege, Coordinator for the Africa Platform at Ghent University, emphasised the broader vision behind the initiative:

“The purpose of these fellowships is to foster structural academic collaboration between Ghent University and partners in Africa. We offer a dedicated amount of seed funding to support short-term mobility, with the aim of building long-term partnerships – through student exchange, joint PhDs, joint funding proposals, and collaborative research publications. 

The UFS is a long-standing partner of Ghent University, especially in Education, Linguistics, and Agriculture. We are delighted that this fellowship has taken place in the field of Data Analysis and Mathematical Modelling - a scientific domain still to be fully developed between our institutions.” 

 

Enhancing UFS’ global research impact 

Dr Aluko’s successful fellowship reflects the high calibre of researchers at the UFS and illustrates the importance of international academic mobility. By securing this competitive opportunity, Dr Aluko not only advanced his own work but also strengthened the UFS’ global research footprint - opening new collaborative avenues and reinforcing the university's growing reputation in health sciences and data-driven research. 

The UFS expresses its sincere gratitude to the Africa Platform of Ghent University and the UFS International Office for their critical support in enabling this milestone. Partnerships such as this are key to fulfilling the UFS’ mission of producing world-class research and fostering meaningful global engagement. 

News Archive

Mathematical methods used to detect and classify breast cancer masses
2016-08-10

Description: Breast lesions Tags: Breast lesions

Examples of Acho’s breast mass
segmentation identification

Breast cancer is the leading cause of female mortality in developing countries. According to the World Health Organization (WHO), the low survival rates in developing countries are mainly due to the lack of early detection and adequate diagnosis programs.

Seeing the picture more clearly

Susan Acho from the University of the Free State’s Department of Medical Physics, breast cancer research focuses on using mathematical methods to delineate and classify breast masses. Advancements in medical research have led to remarkable progress in breast cancer detection, however, according to Acho, the methods of diagnosis currently available commercially, lack a detailed finesse in accurately identifying the boundaries of breast mass lesions.

Inspiration drawn from pioneer

Drawing inspiration from the Mammography Computer Aided Diagnosis Development and Implementation (CAADI) project, which was the brainchild Prof William Rae, Head of the department of Medical Physics, Acho’s MMedSc thesis titled ‘Segmentation and Quantitative Characterisation of Breast Masses Imaged using Digital Mammography’ investigates classical segmentation algorithms, texture features and classification of breast masses in mammography. It is a rare research topic in South Africa.

 Characterisation of breast masses, involves delineating and analysing the breast mass region on a mammogram in order to determine its shape, margin and texture composition. Computer-aided diagnosis (CAD) program detects the outline of the mass lesion, and uses this information together with its texture features to determine the clinical traits of the mass. CAD programs mark suspicious areas for second look or areas on a mammogram that the radiologist might have overlooked. It can act as an independent double reader of a mammogram in institutions where there is a shortage of trained mammogram readers. 

Light at the end of the tunnel

Breast cancer is one of the most common malignancies among females in South Africa. “The challenge is being able to apply these mathematical methods in the medical field to help find solutions to specific medical problems, and that’s what I hope my research will do,” she says.

By using mathematics, physics and digital imaging to understand breast masses on mammograms, her research bridges the gap between these fields to provide algorithms which are applicable in medical image interpretation.

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