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Deep learning algorithms for breast cancer risk analysis

Researcher Raju Gudhe has studied computer science with a focus on intelligent systems. He is now developing deep learning algorithms for breast cancer risk analysis using radiology and clinical data. These algorithms have been trained using massive data sets from Kuopio University Hospital to predict the density of breasts on mammograms.

Algorithms can identify tumour from mammograms. Breast density is one of the most commonly used breast cancer risk factor. The denser the breast, the greater the risk. Deep learning algorithms can assist
radiologists to accurately predict the percentage of breast density.

“We try to localise regions of interest on a mammogram and classify the tumour type based on features
extracted using deep learning algorithms,” says Gudhe, who works as a data analyst at the Institute of
Clinical Medicine of the University of Eastern Finland, in Kuopio.

Mammography, a low dose x-ray imaging technique, is one of the most widely used methods of detecting
early-stage breast cancer. However, mammography is not perfect. Mammograms are not particularly sensitive and can miss cancer cases, or can appear normal even when cancer is present.

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