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Related Experiment Video

Updated: Aug 8, 2025

Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition

Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition

Published on: August 18, 2022

Hyperspectral imaging-based cutaneous wound classification using neighbourhood extraction 3D convolutional neural

Mücahit Cihan¹, Murat Ceylan¹

¹The Department of Electrical and Electronics Engineering, Faculty of Engineering and Natural Sciences, Konya Technical University, Konya, Türkiye.

Biomedizinische Technik. Biomedical Engineering

|March 2, 2023

Summary

Hyperspectral imaging accurately classifies cutaneous wounds using a 3D convolutional neural network. This method effectively distinguishes wounded from normal tissue, regardless of skin color, offering a promising clinical diagnostic tool.

Keywords:

3D CNN hyperspectral imaging neighbourhood extraction wound classification

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Published on: December 8, 2010

Area of Science:

Medical Imaging
Computational Biology
Dermatology

Background:

Hyperspectral imaging (HSI) offers advanced capabilities for medical research and clinical applications.
HSI provides crucial information for characterizing tissue, with distinct spectral signatures for wounded versus normal tissue due to oxygenation changes.

Purpose of the Study:

To classify cutaneous wounds using hyperspectral imaging and a novel neighborhood extraction 3D convolutional neural network (CNN).
To leverage spatial and spectral information from HSI data for accurate wound classification.

Main Methods:

Acquired hyperspectral images of wounded and normal tissues.
Generated 3D cuboids incorporating neighboring pixels to capture spatial and spectral data.
Trained a 3D CNN model to classify tissues based on these cuboids.

Main Results:

The proposed 3D CNN method achieved 99.69% accuracy with a 0.9/0.1 training/testing split and a cuboid spatial dimension of 17.
The method demonstrated superior performance compared to 2D CNNs, even with limited training data.
High classification accuracy for wounded areas was consistently observed.

Conclusions:

Hyperspectral imaging combined with a 3D CNN is a highly effective clinical diagnostic tool for classifying wounded and normal tissues.
The method's success is independent of skin color, as spectral signatures of wounded and normal tissues remain consistent across different ethnic groups.