  Diagnosing erythema migrans (EM) skin lesion, the most common early symptom
of Lyme disease using deep learning techniques can be effective to prevent
long-term complications. Existing works on deep learning based EM recognition
only utilizes lesion image due to the lack of a dataset of Lyme disease related
images with associated patient data. Physicians rely on patient information
about the background of the skin lesion to confirm their diagnosis. In order to
assist the deep learning model with a probability score calculated from patient
data, this study elicited opinion from fifteen doctors. For the elicitation
process, a questionnaire with questions and possible answers related to EM was
prepared. Doctors provided relative weights to different answers to the
questions. We converted doctors evaluations to probability scores using
Gaussian mixture based density estimation. For elicited probability model
validation, we exploited formal concept analysis and decision tree. The
elicited probability scores can be utilized to make image based deep learning
Lyme disease pre-scanners robust.
