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Diagnosing erythema migrans (EM) skin lesion, the most common early symptomof Lyme disease using deep learning techniques can be effective to preventlong-term complications. Existing works on deep learning based EM recognitiononly utilizes lesion image due to the lack of a dataset of Lyme disease relatedimages with associated patient data. Physicians rely on patient informationabout the background of the skin lesion to confirm their diagnosis. In order toassist the deep learning model with a probability score calculated from patientdata, this study elicited opinion from fifteen doctors. For the elicitationprocess, a questionnaire with questions and possible answers related to EM wasprepared. Doctors provided relative weights to different answers to thequestions. We converted doctors evaluations to probability scores usingGaussian mixture based density estimation. For elicited probability modelvalidation, we exploited formal concept analysis and decision tree. Theelicited probability scores can be utilized to make image based deep learningLyme disease pre-scanners robust.

Expert Opinion Elicitation for Assisting Deep Learning based Lyme Disease Classifier with Patient Data