• Matlab
  • Simulink
  • NS3
  • OMNET++
  • NS2

Crowdsourcing Biological Specimen Identification

Existing technology may provide a solution to the intractable problem of the unmet demand in rural areas for the identification of biological specimens. The acquisition and transmission of biological specimens, revealed through low-cost microscopy, to a server would be viewed and analyzed by autonomous members of a crowdsource community, where, subject to a system of quality control, populations that do not have access to medical diagnostic facilities might have the full advantages of medical analyses. In our proposed system, a smartphone is equipped with a low-cost microscope interface.

This enables health-care workers to obtain digital images of biological specimens, bacteria, or tissue cultures that are then transmitted to a cloud server. These image data can be made available, via crowd-sourcing, for experts to provide a consensus on the imaged specimen. The opinions of crowdsourced experts can be based in part on their scientific peer ranking and professional qualifications and in part on their history of contributions and peer ranking within the crowdsourcing community. Different opinions as to the nature of the specimen provided can be weighed by the crowdsourcing software engine that then presents the conclusions to the originating health-care worker.

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