Improve diagnostics of typhoid through Open Science: An Artificial Intelligence-based technique
summary
Describes developing a low-cost mobile diagnostic system for typhoid that combines convolutional neural networks on blood serum microscope images with a Rough Set Theory classifier operating on structured symptom data. The system connects a smartphone camera to an optical microscope to classify slides as Normal or Infected and uses the Rough Set output to confirm diagnosis and estimate disease severity as a decision-support tool. The talk also outlines the chosen AI tools, data sources, and considerations around values, ethics and governance for deployment.
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