Detecting malaria with deep learning
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Artificial intelligence (AI) and open source tools, technologies, and frameworks are a powerful combination for improving society. ‘Health is wealth’ is perhaps a cliche, yet it’s very accurate! In this article, we will examine how AI can be leveraged for detecting the deadly disease malaria with a low-cost, effective, and accurate open source deep learning solution.
While I am neither a doctor nor a healthcare researcher and I’m nowhere near as qualified as they are, I am interested in applying AI to healthcare research. My intent in this article is to showcase how AI and open source solutions can help malaria detection and reduce manual labor. Thanks to the power of Python and deep learning frameworks like TensorFlow, we can build robust, scalable, and effective deep learning solutions.
Because these tools are free and open source, we can build solutions that are very cost-effective and easily adopted and used by anyone. Let’s get started! Malaria is a deadly, infectious, mosquito-borne disease caused by Plasmodium parasites that are transmitted by the bites of infected female Anopheles mosquitoes.
There are five parasites that cause malaria, but two types—P. falciparum and P. vivax—cause the majority of the cases.
Source: opensource.com
