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Updated: Aug 20, 2025

Deep Neural Networks for Image-Based Dietary Assessment

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Published on: March 13, 2021

Multi-modal wound classification using wound image and location by deep neural network.

D M Anisuzzaman¹, Yash Patel¹, Behrouz Rostami²

¹Department of Computer Science, University of Wisconsin-Milwaukee, Milwaukee, WI, USA.

Scientific Reports

|November 22, 2022

Summary

This study introduces a deep learning wound classifier using images and location data. The multi-modal approach accurately categorizes diabetic, pressure, surgical, and venous ulcers, improving diagnostic efficiency.

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Published on: November 30, 2022

Area of Science:

Medical Informatics
Artificial Intelligence in Healthcare
Computational Pathology

Background:

Accurate wound classification is crucial for effective diagnosis and treatment planning.
Current methods can be time-consuming and costly, necessitating more efficient solutions.
Deep learning offers potential for automated analysis of complex medical data.

Purpose of the Study:

To develop a deep neural network-based multi-modal classifier for wound categorization.
To integrate wound images and location data for improved classification accuracy.
To create a body map tool for efficient wound location tagging.

Main Methods:

Development of a multi-modal deep neural network integrating image and location features.
Creation of three specialized datasets with wound images and corresponding body map locations.
Concatenation of image-based and location-based classifier outputs for final classification.

Main Results:

Maximum accuracy for mixed-class classification (including background/normal skin) ranged from 82.48% to 100%.
Maximum accuracy for wound-class classification (diabetic, pressure, surgical, venous ulcers) ranged from 72.95% to 97.12%.
The proposed multi-modal network demonstrated significant performance improvements over existing literature.

Conclusions:

The developed multi-modal classifier effectively categorizes various wound types using image and location data.
The system offers a promising tool for assisting wound specialists, reducing costs and time.
This approach represents a significant advancement in automated wound diagnosis and classification.