2022/08/30 by Sk Imran Hossain, Hossain, Sk Imran, Jocelyn de Goër de Herve +17
Computer Science · Decision Sciences · Engineering · Medicine · #Artificial Intelligence (cs.AI) #Data-Driven Disease Surveillance #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Reliability and Agreement in Measurement #cs.AI #eess.IV #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2208.14384
arxiv created 2022/08/30 · openalex publication_date 2022/08/30 · arxiv updated 2022/08/31 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.