Aplicação de imagens sintéticas para otimização de modelos computacionais de detecção do estrabismo

Abstract

Strabismus is among the eye diseases that most lead to blindness or low vision, affecting about 4 % of the world population. Fortunately, the disease can be treated. Diagnosis even in the first moments of its manifestation dramatically increases the possibility of successful treatment. There are several proposals in the scientific literature for detecting and supporting the diagnosis of pathology, however, we have not found studies that seek to propose means of optimization for these techniques. This article presents a methodology for optimizing supervised strabismus detection models by increasing data using realistic synthetic samples. In evaluation, the proposed technique resulted in a gain of 7 % accuracy.