Impacto da Resolução na Detecção de Retinopatia Diabética com uso de Deep Learning

• Francis Moreira UFRGS
• Beatriz Schaan HCPA
• Josiane Schneiders HCPA
• Mateus Reis HCPA
• Matheus Serpa UFRGS
• Philippe Navaux UFRGS

DOI: https://doi.org/10.5753/sbcas.2020.11546

Abstract

In the context of healthcare systems, preventive screening is one of the most effective ways to prevent disease progression. Most diseases can be treated when detected in their initial stages. The demand for preventive screening is increasing, and this demand cannot be efficiently covered by the overloaded medical doctors. Therefore, there is a current need for a methodology to automate and increase the efficiency of preventive screening. In this paper, we discuss the development of deep convolutional neural networks to detect diabetic retinopathy, and the impact of image and network resolution on prediction accuracy. We have achieved 0.93 area under the receiver operating characteristic curve by increasing the analysis resolution of the inception v3 architecture.

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