Evaluating Skin Tone Fairness in Convolutional Neural Networks for the Classification of Diabetic Foot Ulcers
The present paper investigates the application of convolutional neural networks (CNNs) for the classification of diabetic foot ulcers, using VGG16, VGG19 and MobileNetV2 architectures. The primary objective is to develop and compare deep learning models capable of accurately identifying ulcerated re...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-07-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/15/8321 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849770685142925312 |
|---|---|
| author | Sara Seabra Reis Luis Pinto-Coelho Maria Carolina Sousa Mariana Neto Marta Silva Miguela Sequeira |
| author_facet | Sara Seabra Reis Luis Pinto-Coelho Maria Carolina Sousa Mariana Neto Marta Silva Miguela Sequeira |
| author_sort | Sara Seabra Reis |
| collection | DOAJ |
| description | The present paper investigates the application of convolutional neural networks (CNNs) for the classification of diabetic foot ulcers, using VGG16, VGG19 and MobileNetV2 architectures. The primary objective is to develop and compare deep learning models capable of accurately identifying ulcerated regions in clinical images of diabetic feet, thereby aiding in the prevention and effective treatment of foot ulcers. A comprehensive study was conducted using an annotated dataset of medical images, evaluating the performance of the models in terms of accuracy, precision, recall and F1-score. VGG19 achieved the highest accuracy at 97%, demonstrating superior ability to focus activations on relevant lesion areas in complex images. MobileNetV2, while slightly less accurate, excelled in computational efficiency, making it a suitable choice for mobile devices and environments with hardware constraints. The study also highlights the limitations of each architecture, such as increased risk of overfitting in deeper models and the lower capability of MobileNetV2 to capture fine clinical details. These findings suggest that CNNs hold significant potential in computer-aided clinical diagnosis, particularly in the early and precise detection of diabetic foot ulcers, where timely intervention is crucial to prevent amputations. |
| format | Article |
| id | doaj-art-3e9db4ce8c9e47f7800dc8a71cdbacf0 |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-3e9db4ce8c9e47f7800dc8a71cdbacf02025-08-20T03:02:55ZengMDPI AGApplied Sciences2076-34172025-07-011515832110.3390/app15158321Evaluating Skin Tone Fairness in Convolutional Neural Networks for the Classification of Diabetic Foot UlcersSara Seabra Reis0Luis Pinto-Coelho1Maria Carolina Sousa2Mariana Neto3Marta Silva4Miguela Sequeira5ISEP, Polytechnic of Porto, 4249-015 Porto, PortugalISEP, Polytechnic of Porto, 4249-015 Porto, PortugalISEP, Polytechnic of Porto, 4249-015 Porto, PortugalISEP, Polytechnic of Porto, 4249-015 Porto, PortugalISEP, Polytechnic of Porto, 4249-015 Porto, PortugalISEP, Polytechnic of Porto, 4249-015 Porto, PortugalThe present paper investigates the application of convolutional neural networks (CNNs) for the classification of diabetic foot ulcers, using VGG16, VGG19 and MobileNetV2 architectures. The primary objective is to develop and compare deep learning models capable of accurately identifying ulcerated regions in clinical images of diabetic feet, thereby aiding in the prevention and effective treatment of foot ulcers. A comprehensive study was conducted using an annotated dataset of medical images, evaluating the performance of the models in terms of accuracy, precision, recall and F1-score. VGG19 achieved the highest accuracy at 97%, demonstrating superior ability to focus activations on relevant lesion areas in complex images. MobileNetV2, while slightly less accurate, excelled in computational efficiency, making it a suitable choice for mobile devices and environments with hardware constraints. The study also highlights the limitations of each architecture, such as increased risk of overfitting in deeper models and the lower capability of MobileNetV2 to capture fine clinical details. These findings suggest that CNNs hold significant potential in computer-aided clinical diagnosis, particularly in the early and precise detection of diabetic foot ulcers, where timely intervention is crucial to prevent amputations.https://www.mdpi.com/2076-3417/15/15/8321convolutional neural networksmedical image classificationdiabetic foot ulcersdeep learning |
| spellingShingle | Sara Seabra Reis Luis Pinto-Coelho Maria Carolina Sousa Mariana Neto Marta Silva Miguela Sequeira Evaluating Skin Tone Fairness in Convolutional Neural Networks for the Classification of Diabetic Foot Ulcers Applied Sciences convolutional neural networks medical image classification diabetic foot ulcers deep learning |
| title | Evaluating Skin Tone Fairness in Convolutional Neural Networks for the Classification of Diabetic Foot Ulcers |
| title_full | Evaluating Skin Tone Fairness in Convolutional Neural Networks for the Classification of Diabetic Foot Ulcers |
| title_fullStr | Evaluating Skin Tone Fairness in Convolutional Neural Networks for the Classification of Diabetic Foot Ulcers |
| title_full_unstemmed | Evaluating Skin Tone Fairness in Convolutional Neural Networks for the Classification of Diabetic Foot Ulcers |
| title_short | Evaluating Skin Tone Fairness in Convolutional Neural Networks for the Classification of Diabetic Foot Ulcers |
| title_sort | evaluating skin tone fairness in convolutional neural networks for the classification of diabetic foot ulcers |
| topic | convolutional neural networks medical image classification diabetic foot ulcers deep learning |
| url | https://www.mdpi.com/2076-3417/15/15/8321 |
| work_keys_str_mv | AT saraseabrareis evaluatingskintonefairnessinconvolutionalneuralnetworksfortheclassificationofdiabeticfootulcers AT luispintocoelho evaluatingskintonefairnessinconvolutionalneuralnetworksfortheclassificationofdiabeticfootulcers AT mariacarolinasousa evaluatingskintonefairnessinconvolutionalneuralnetworksfortheclassificationofdiabeticfootulcers AT mariananeto evaluatingskintonefairnessinconvolutionalneuralnetworksfortheclassificationofdiabeticfootulcers AT martasilva evaluatingskintonefairnessinconvolutionalneuralnetworksfortheclassificationofdiabeticfootulcers AT miguelasequeira evaluatingskintonefairnessinconvolutionalneuralnetworksfortheclassificationofdiabeticfootulcers |