Automatic Classification of Diabetic Foot Ulcers Using Computer Vision Techniques

Autor:	Yusely Ruiz-Gonzalez, Roberto Díaz-Amador, José Daniel López-Cabrera, Alberto Taboada-Crispi
Rok vydání:	2021
Předmět:	Svm classifier ComputingMethodologies_PATTERNRECOGNITION business.industry Computer science Feature extraction Pattern recognition (psychology) medicine Computer vision Artificial intelligence medicine.disease business Diabetic foot Convolutional neural network
Zdroj:	Progress in Artificial Intelligence and Pattern Recognition ISBN: 9783030896904 IWAIPR
DOI:	10.1007/978-3-030-89691-1_29
Popis:	Diabetic foot ulcers are one of the common complications that diabetic patients present. Poorly treated lesions can lead to the amputation of the limbs and even cause death. Therefore, the identification and follow-up of the lesions are of vital importance to apply a timely treatment. In this study, we performed the automatic classification of images of diabetic foot ulcers using computer vision techniques. We evaluated different approaches to traditional computer vision techniques and feature extraction from a convolution neural network. An SVM classifier using features extracted by the CNN Densenet201 obtained the best results. The results achieved here outperformed those reported in the literature for similar problems in terms of the F1score measure. That shows that the proposed alternative of combining a pre-trained CNN model as a feature extraction method and then using automatic classifiers is satisfactory in this task.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::773e99cae4b90aff83bc6e34b57c7b85 https://doi.org/10.1007/978-3-030-89691-1_29 Zobrazit plný text záznamu