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06 August 2025 | Story Lilitha Dingwayo | Photo Supplied
Mobi Readathon
Attending the MobiReadathon (left to right): Rasesemola Elias, Principal Librarian, Fezile Dabi District; Mzwandile Radebe, Principal Librarian, Thabo Mofutsanyana District Municipality; Jeannet Molopyane, Director, UFS Library and Information Services; Nomabhaso Ramugondo, Director, Free State Provincial Library Services; Elmari Kruger, Deputy Director, Motheo District Municipality; Larshan Naicker, Deputy Director, UFS Library and Information Services; Adele Bezuidenhout, Deputy Director, Fezile Dabi District Municipality; Henna Adendorff, Assistant Manager, Free State Provincial Library Services; and Thandi Gxabu, Librarian, Free State Provincial Library Services.

The University of the Free State (UFS) Department of Library and Information Services recently hosted the 2025 MobiReadathon competition, a digital reading initiative established by the City of Johannesburg Library Services. Now a national programme involving all nine provinces, the competition was introduced to Grade 8 high school learners in the Free State for the first time, with UFS playing a central role in supporting digital literacy and community empowerment.

Held at the UFS Sasol Library on 25 July 2025, the Free State leg of the 2025 MobiReadathon brought together 50 Grade 8 learners from across the province. The room buzzed with excitement as the young readers engaged in digital reading tasks and trivia challenges via mobile devices.

“I never liked reading, and because I am not fluent in English I thought I should start reading, and this initiative has been helpful for me,” said Bohlokwa Dikoetsing, a learner at Bodibeng Secondary School.

Tshepo Kgaola, also a participant, said the most exciting part of the competition was when his team won a voucher for reading after they created a story using artificial intelligence (AI).

“This initiative is part of our digital transformation agenda for public libraries,” said Nomabhaso (Rasby) Ramugondo, Director of the Free State Provincial Library Services. Ramugondo emphasised the issue of reading with understanding in South Africa, a priority that she hopes to see eradicated through programmes like the MobiReadathon. “We had asked Jeff Nyoka from the City of Johannesburg Library Services to come and do a presentation about digital literacy,” she explained. “It was then that a team of digital transformers was established to come up with initiatives like the Reja Buka Reading Festival that will help learners – and that is how the collaboration on the MobiReadathon came about in Free State.” 

“The essence of this collaboration is to promote reading development,” said Tebogo Msimango, Senior Librarian for E-learning Programmes at the City of Johannesburg. Just like Ramugondo, Msimango explains the need to promote digital reading due to the issue of learners not being able to read for meaning.

“The outcome I would like for this initiative is for learners to discover themselves and come to an understanding that with reading, one could go far,” Msimango said. “These collaborations also help with making the learners realise that they could also come into the university space, and a good example is the tour that they were taken on around the library.”

UFS Library Services played a pivotal role in facilitating the event, offering logistical support. As part of its community engagement initiatives, the university continues to collaborate on programmes that uplift local youth and promote literacy through innovation.

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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