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27 October 2025
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Story Sefako Mokhosoa
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Photo Supplied
Ten Grade 12 learners from Mampoi Secondary School in Phuthaditjhaba.
On 1 October 2025, the Projects and Innovation Directorate in the Faculty of Education at the University of the Free State (UFS) proudly hosted a certificate ceremony to honour ten Grade 12 learners from Mampoi Secondary School in Phuthaditjhaba on the Qwaqwa Campus. These learners completed a Skills Development Initiative and Workshop Series focused on digital literacy and ICT skills – a programme designed to equip rural youth with the tools they need to thrive in a digital world.
The initiative, which ran from May to August 2025, was made possible through a strategic partnership with BANKSETA to bridge the digital divide in rural communities. The learners received hands-on training in essential digital tools. Each learner also received a tablet to support continued learning and personal growth beyond the classroom.
The Director of the office in the Faculty of Education, Dr Kwazi Magwenzi, stressed that digital skills promote independence and self-directed learning. “Grade 12 is a time when learners should manage their studies, meet deadlines, and explore their options,” she said. “Digital fluency supports that autonomy. It enables learners to use online research, interactive tools, e-learning, and collaboration platforms to make learning more effective, flexible, and aligned with their pace and style. In Grade 12, where the stakes are high – with exams, tertiary entrance, and career choices – this ability helps learners become more self-directed, confident, and equipped.”
The programme not only built learners’ confidence in using ICT tools for learning and communication but also prepared them for the technologically driven environments they will encounter in institutions of higher learning.
Beyond developing digital skills, the project offered learners valuable exposure to the university environment, as their training took place on campus. Inspired by the success of this pilot, the Faculty of Education now aims to expand the initiative to reach more schools and learners across the region. The vision is to scale up access to digital education and empower more young people in rural areas with the skills necessary for academic and professional success.
This ceremony marked the conclusion of a successful training programme and the beginning of a long-term commitment to digital empowerment and lifelong learning in rural communities.
Mathematical methods used to detect and classify breast cancer masses
2016-08-10
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.