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13 July 2018 Photo Johan Roux
Sikhululekile Luwaca named 100 Young Mandelas of the future
Sikhululekile Luwaca was recently named as one of 100 Young Mandelas of the future by News24 for his embodiment of Nelson Mandela’s characteristics.
On Tuesday 3 July 2018, News24.com announced the 100 Young Mandelas of the future. Among those nominated was Sikhululekile Luwaca, a former president of the Student Representative Council (SRC) at the University of the Free State (UFS).
 
“It is humbling. I embrace collective action and it would be unfair not to appreciate all the great minds I have encountered over the years and had the privilege to work with. Our individual progress can never be separated from that of the community. It is no longer I that lives, but us, we,” said Luwaca.

Six million readers nominated 1 000 South Africans from all walks of life who could be considered Mandelas of the future. Luwaca emerged in the Visionary category as one of the 100 who made the cut. The initiative was inspired by what would have been Nelson Mandela’s 100th birthday on 18 July 2018. “News24 set out to honour 100 young South Africans who embody the characteristics Mandela was best known for,” said a statement by News24.

While he was the SRC president, Luwaca’s office played a critical role in raising R1.2 million for underprivileged students. He continues to make major strides as the current chairperson of the UFS African National Congress Youth League (ANCYL). 

His social and political influence goes back to when a 13-year-old Luwaca founded an association that sought to address school dropouts in rural areas. In high school, the young philanthropist established an organisation that collected and distributed food for needy elders of Cathcart township in the Eastern Cape. For five years Luwaca served the Student Christian Organisation as chairperson. In 2013, he co-founded the Ubuntu School Project that donated 100 full school uniforms to Phomolong High School learners in Tembisa.

Later on as a UFS student, Luwaca helped found the Hand2Hand Student Association which drives fundraising initiatives, as well as the collection of non-perishable food items and second-hand textbooks for disadvantaged students. In 2015 he was elected a Residence Committee representative for House Outeniqua and SRC: Dialogue and Association. 

Luwaca was instrumental in facilitating a series of dialogues on transformation such as the Fees Must Fall movement and the Shimla Park incident.

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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