Diabetic Foot Ulcers Classification using a fine-tuned CNNs Ensemble

• Type: Article in International Conference Proceedings Book
• Date: 2022

Authors

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

Conference proceedings International

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

Pages: 282-287

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

ELECTR NETWORK, JUL 21-23, 2022

Indexing

ISI Web of Knowledge - 0 Citations

Scopus

Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 6