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30 July 2020 | Story Valentino Ndaba | Photo Anja Aucamp
Dr Fumane Khanare opted to integrate poetry into her teaching practice, using innovative ways to keep the curriculum afloat and interesting at the same time.

The Coronavirus (COVID-19) lockdown has severely affected teaching and learning. Lecturers and students alike have been challenged to explore innovative ways to keep the curriculum afloat and interesting at the same time. Dr Fumane Khanare, Senior Lecturer in the Faculty of Education, has opted to integrate poetry into her teaching practice. Her Community Psychology students have shifted over the past few months from merely interacting with the course material to generating their own content.

Learning in the times of lockdown

According to Dr Khanare, the psycho-social impact of COVID-19 remains unknown as the world grapples with a backlog of information, accompanied by loss and grief. However, collaborative strides are being made in the right direction, considering that this is unchartered territory. “Recommendations advocating for online teaching and learning, bidding for free data, and laptops for the majority of students, especially those at the peripheries of a mainstream economy – and of course physical distancing-adhering wellness programmes – may enable effective teaching and learning.” 

Why poetry?

“Lurched in at the deep end and taking into account the students who are not well-equipped with the integration of information and communications technology in learning, is significant. This realisation led me to seek ways to help my students develop a deeper understanding and critical-thinking skills, as well as becoming self-motivated students amid COVID-19,” explained Dr Khanare.

Students were first tasked with analysing the poetry of Butler-Kisber (2002). Thereafter, they were required to write poems about COVID-19, underpinned by the Community Psychology in Education module. “The activity provided students with an opportunity to use and reinforce concepts learnt prior to the lockdown, monitor their own understanding and progress, plus motivate them to come to the lecture prepared – a function known as co-creators of knowledge,” she said.

The artistic creations of these students were circulated among peers for review, allowing them to move from the peripheries to the centre of knowledge production amid a pandemic. 

Digitising the education space

Beyond the classroom, Dr Khanare will attend the 2020 Women Academics in Higher Education Virtual Symposium. As the co-convener of the World Education Research Association-International Research Network, she continues to ensure that research-related activities continue, despite a ban on international travel.

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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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