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Updated: Jan 7, 2026

Fabrication and Characterization of a Conformal Skin-like Electronic System for Quantitative, Cutaneous Wound Management

Fabrication and Characterization of a Conformal Skin-like Electronic System for Quantitative, Cutaneous Wound Management

Published on: September 2, 2015

Automating wound assessment: convolutional neural network-based mobile application for SINBAD classification system.

Farideh Mostafavi¹, Sujit Kumar Das², Mohammad Reza Amini³

¹Department of Epidemiology, School of Public Health and Safety, Shahid Beheshti University of Medical Sciences, Shahid Shahriari Sq., Student Blvd., Valenjak, Tehran, 1983969411 Iran.

Journal of Diabetes and Metabolic Disorders

|December 29, 2025

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This summary is machine-generated.

A new mobile app uses MobileNetV3 Small to automatically classify diabetic foot ulcer (DFU) components using the SINBAD system. This efficient tool improves DFU assessment in clinical settings.

Area of Science:

Medical Imaging
Artificial Intelligence
Computational Biology

Background:

Diabetic foot ulcer (DFU) assessment is crucial for clinical decisions but often lacks specialist access.
The SINBAD system is a standard for DFU classification.

Purpose of the Study:

To develop and evaluate a mobile application for automated DFU classification using a lightweight Convolutional Neural Network (CNN).
To assess the performance of MobileNetV3 Small in classifying five SINBAD components for DFU.

Main Methods:

A dataset of 996 clinician-labeled DFU images was utilized.
A MobileNetV3 Small model was trained to classify five SINBAD components.
Performance was evaluated using accuracy, F1 score, precision, recall, and AUC, and compared to VGG16, ResNet50, and DenseNet121.

Keywords:

Convolutional neural networks Diabetic foot ulcer MobilenetV3 small SINBAD

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Fabrication and Characterization of a Conformal Skin-like Electronic System for Quantitative, Cutaneous Wound Management

Fabrication and Characterization of a Conformal Skin-like Electronic System for Quantitative, Cutaneous Wound Management

Published on: September 2, 2015

Main Results:

MobileNetV3 Small achieved high F1 scores for Bacterial Infection (93.1%), Area (89.8%), and Neuropathy (86.2%), with excellent recall.
For Ischemia and Depth, MobileNetV3 Small showed moderate F1 scores (74.4%, 61.6%) and AUCs (84.3%, 80.3%), outperforming VGG16.
The compact MobileNetV3 Small model demonstrated recall comparable to or exceeding larger models, suitable for sensitive detection.

Conclusions:

MobileNetV3 Small provides a practical and efficient solution for mobile-based DFU assessment.
The app's strong recall and compact architecture facilitate deployment in resource-limited settings for consistent SINBAD classification.