Comparing YOLO and Detectron2 models for automatic extracting patients information from leprosy assessment form
Anthony Alexandre Morais Militão, Hilson Gomes Vilar de Andrade, Kayo Henrique de Carvalho Monteiro, Élisson da Silva Rocha, Patrícia Takako Endo · 2025
Leprosy, caused by Mycobacterium leprae, remains a global challenge, requiring strategies to achieve disease elimination by 2030. In Brazil, the Simplified Neurological Assessment (from Portuguese Avaliação Neurológica Simplificada, ANS) is mandatory for suspected cases; however, the form is still manually fulfilled, which limits the use of data. This study evaluates computer vision models (YOLOv8x, YOLO11x, Faster R-CNN) for detecting hand and foot sensitivity regions from ANS forms. All models were evaluated based on precision, recall, mean average precision (mAP) and confusion matrix. YOLO variants achieved over 94% precision and 84% recall across all classes. Automating ANS data extraction can facilitate the creation of structured datasets, enhancing disease monitoring and enabling the train of predictive models.