Diabetic Foot Ulcers Classification using a fine-tuned CNNs Ensemble

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2022

Autores

Santos, E (Autor) Outra; Santos, F (Autor) Outra; Dallyson, J (Autor) Outra; Aires, K (Autor) Outra; João Manuel R. S. Tavares (Autor) FEUP; Veras, R (Autor) Outra

Ata de Conferência Internacional

Título: 2022 IEEE 35TH INTERNATIONAL SYMPOSIUM ON COMPUTER-BASED MEDICAL SYSTEMS (CBMS)

Páginas: 282-287

35th IEEE International Symposium on Computer-Based Medical Systems (CBMS)

ELECTR NETWORK, JUL 21-23, 2022

Indexação

ISI Web of Knowledge - 0 Citações

Scopus

Outras Informações

ID Authenticus: P-00X-7J1

DOI: 10.1109/cbms55023.2022.00056

Abstract (EN): Diabetic Foot Ulcers (DFU) are lesions in the foot region caused by diabetes mellitus. It is essential to define the appropriate treatment in the early stages of the disease once late treatment may result in amputation. This article proposes an ensemble approach composed of five modified convolutional neural networks (CNNs) - VGG-16, VGG-19, Resnet50, InceptionV3, and Densenet-201 - to classify DFU images. To define the parameters, we fine-tuned the CNNs, evaluated different configurations of fully connected layers, and used batch normalization and dropout operations. The modified CNNs were well suited to the problem; however, we observed that the union of the five CNNs significantly increased the success rates. We performed tests using 8,250 images with different resolution, contrast, color, and texture characteristics and included data augmentation operations to expand the training dataset. 5-fold cross-validation led to an average accuracy of 95.04%, resulting in a Kappa index greater than 91.85%, considered Excellent.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 6