Uma Avaliação de Arquiteturas de Aprendizado Profundo para a Classificação de Úlceras do Pé Diabético

• Francico Santos UFPI
• Rodrigo Veras UFPI
• Elineide Santos UFPI
• Maila Lima Claro UFPI
• Luís Henrique Vogado UFPI
• Márcia Ito Faculdade de Tecnologia de São Paulo
• Andrea Bianchi UFOP

DOI: https://doi.org/10.5753/sbcas.2021.16076

Abstract

A complication caused by diabetes mellitus is the appearance of wounds in the feet called diabetic foot ulcers. Late treatment can lead to the onset of infection or ischemia of the ulcer, which, in an advanced stage, can cause the amputation of the lower limbs. In this work, a comparison of the performance of several pre-trained deep learning architectures in the classification of images of diabetic foot ulcers was carried out. Our assessment considered four scenery, Three binaries Healthy vs Ulcers; Healthy vs Ischemia; Healthy vs Infection and one multiclass Healthy vs Ulcer vs Ischemia vs Infection vs Infection and Ischemia. The results showed that our proposal could classify such images since the Kappa index reached values considered "Excellent" in the tests carried out. However, for the multiclass problem, it is still necessary to improve the use of these techniques.

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