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06 March 2020 | Story Valentino Ndaba | Photo Stephen Collett
Lesetja Kganyago, Governor of the South African Reserve Bank
Reserve Bank Governor, Lesetja Kganyago, presented a public lecture at the UFS on 4 March 2020.

With a 7% fiscal deficit on the Gross Domestic Product (GDP) projected by the National Treasury for the 2020/21 financial year, it would not take long to arrive at a dangerous level of debt at the rate that South Africa is borrowing. Although the South African Reserve Bank Governor, Lesetja Kganyago, does not consider a debt to GDP rate of 60% a disaster, he did express his concern regarding the country’s fiscal deficits being over 6% of the GDP.

Governor Kganyago presented a public lecture at the University of the Free State (UFS) on 4 March 2020, focusing on how we should use macro-economic policy and its role in our economic growth problem.

Unsustainable policies 
South Africa’s fiscal situation is not about tight monetary policy. According to the Governor: “Weak growth is endogenous in our fiscal problems. We cannot keep doing what we are doing and hope that growth will recover and save us. Growth is low, in large part, because of unsustainable policy.”

Avoiding an impending crisis
To address the problem, as a policymaker with more than 20 years’ experience, the Governor suggested that the recommendations made by Minister Tito Mboweni be taken into consideration. “The Minister of Finance, Tito Mboweni, is a man who says things that are true even when they are unpopular. His message is that we have to reduce spending and he is right to put this at the centre of our macro-economic debate,” said Governor Kganyago.

The state needs a radical economic turnaround strategy which is able to diminish the risk of losing market access and being forced to ask the International Monetary Fund for help. Governor Kganyago is positive that such a reformative tactic would go beyond monetary policy and ensure that the interest bill ceases to claim more of South Africa’s scarce resources. 

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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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