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Improving malaria detection through computer vision and machine learning

Electrical engineering doctoral students Charles Delahunt and Mayoore Jaiswal are applying their skills in computer vision and machine learning to the fight against malaria, a disease that affects over 200 million people each year and is one of the most severe public health problems globally. Working with a team at Intellectual Ventures (IV) Lab and with support from the Global Good Fund, they have developed Autoscope, a low-cost, portable and automated device for diagnosing malaria.¬†For Jaiswal, who grew up in Sri Lanka where mosquito-transmitted diseases were and, in some cases, continue to be a serious threat, the project’s social impact is key.

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