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11 November 2022 | Story Anthony Mthembu | Photo Barend Nagel
Siphilangenkosi Dlamini
Siphilangenkosi Dlamini – selected by Inside Education and the NYDA as one of South Africa’s 100 Shining Stars for 2022.

Siphilangenkosi Dlamini, a fourth-year Governance and Political Transformation student at the University of the Free State (UFS), has been selected as one of South Africa’s 100 Shining Stars for 2022 by Inside Education, in partnership with the National Youth Development Agency (NYDA). 

“I was more surprised than anything, but also very honoured,” he said. Dlamini, who made it into the Civil Society and Youth category, was chosen from a pool of 800 applicants for his remarkable work with the Help a Student initiative, and his services as the former secretary of the Southern Africa Scout Youth Forum. Although he could not attend the award ceremony held in Johannesburg on 20 October 2022 in person, Dlamini did receive a certificate. “What we do a lot of the time isn’t for recognition and it’s not necessarily for awards; but getting recognised motivates and assures me that the work we are doing has an impact,” he expressed.

The Help a Student Initiative

In the early stages of the COVID-19 pandemic, Dlamini recognised a rise in food insecurity among his fellow students. This set him on a path to source funding for the establishment of the project. 

The Help a Student initiative aimed to curb food insecurity through the provision of food parcels to UFS students who were in need. The project, which ran from 2020 until early 2021, managed to distribute food parcels to nearly 250 students per month. The initiative did not only assist students who were on campus. The selected applicants who were at home or off campus also received digital food vouchers, which allowed for the purchasing of food items at Pick n Pay and/or Shoprite.

“Food security is something that I am passionate about. I grew up in a community where it was a massive issue.

However, in the past I was not empowered enough to know how to solve it. Therefore, when the opportunity presented itself to do something about it, I took it with both hands,” Dlamini expressed.

Although the recognition was not expected, Dlamini maintains that such platforms are imperative, as “they demonstrate that young people are doing something to improve the country in the different capacities they are in”.

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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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