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Related Experiment Video

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Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging

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Automatic Diabetic Foot Ulcer Recognition Using Multi-Level Thermographic Image Data.

Ikramullah Khosa¹, Awais Raza¹, Mohd Anjum²

¹Department of Electrical and Computer Engineering, COMSATS University Islamabad, Lahore Campus, Lahore 54000, Pakistan.

Diagnostics (Basel, Switzerland)

|August 26, 2023

Summary

This summary is machine-generated.

Diabetic foot ulcers (DFUs) pose a significant risk for amputation. This study shows a custom CNN model using thermography accurately detects DFUs, outperforming other methods and improving early diagnosis.

Keywords:

deep learning diabetes mellitus diabetic foot ulcer machine learning thermograms

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Area of Science:

Medical imaging
Computational diagnostics
Diabetes mellitus research

Background:

Diabetic foot ulcers (DFUs) are a severe complication of diabetes mellitus (DM), with a 15-25% lifetime risk and up to 85% risk of lower limb amputation.
Early and accurate diagnosis of DFUs is crucial to prevent severe outcomes.
Thermography offers a non-invasive method to detect temperature changes associated with planter ulcers in diabetic feet.

Purpose of the Study:

To evaluate the efficacy of machine learning and deep learning models for detecting diabetic foot ulcers (DFUs) using thermographic data.
To develop and validate a custom Convolutional Neural Network (CNN) model for improved DFU recognition.
To compare the performance of image-level, patch-level, and combined thermogram data for DFU detection.

Main Methods:

Utilized publicly available thermographic image datasets comprising control and diabetic patient groups.
Employed machine learning classifiers with hand-crafted features and deep learning models (ResNet50, DenseNet121) for DFU recognition.
Developed and tested a custom CNN model using image-level, patch-level, and combined thermogram data.

Main Results:

The proposed custom CNN model achieved superior performance in Area Under the Curve (AUC) and accuracy compared to existing models.
Both machine learning and deep learning approaches demonstrated higher recognition accuracy using image-level thermograms versus patch-level or combined data.
The custom CNN model significantly outperformed state-of-the-art methods in DFU detection.

Conclusions:

A custom CNN model utilizing thermographic data shows high accuracy and effectiveness for diabetic foot ulcer detection.
Image-level thermogram analysis provides superior diagnostic accuracy for DFUs compared to patch-level or combined approaches.
This approach holds promise for improving early diagnosis and management of diabetic foot complications, potentially reducing amputation rates.