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Simultaneous Evaluation of Cerebral Hemodynamics and Light Scattering Properties of the In Vivo Rat Brain Using Multispectral Diffuse Reflectance Imaging

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Deep convolutional neural network based hyperspectral brain tissue classification.

P Poonkuzhali¹, K Helen Prabha¹

¹Department of Electronics and Communication Engineering, R.M.D. Engineering College, Tamilnadu, India.

Journal of X-Ray Science and Technology

|May 14, 2023

Summary

This summary is machine-generated.

A novel 3D-CNN model enhances hyperspectral brain tissue classification accuracy to 97%, outperforming 2D-CNN and SVM models. This advancement addresses challenges in processing high-dimensional medical imaging data.

Keywords:

Hyperspectral image classification brain tumor detection convolutional neural network high dimensional features machine learning

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Area of Science:

Medical imaging
Neuroscience
Computer vision

Background:

Hyperspectral brain tissue imaging aids brain science research.
High-dimensional hyperspectral image (HSI) processing is challenging due to limited training samples.

Purpose of the Study:

To introduce a 3D-CNN model for processing spatial and temporal features in HSI.
To improve brain tumor image classification performance.

Main Methods:

Implemented a 3D-CNN model for high-dimensional data processing.
Pre-processed HSI data including calibration and spectral correction.
Extracted spectral and spatial features from HSI.
Validated the model using the Benchmark Vivo human brain HSI dataset.

Main Results:

The 3D-CNN model achieved 97% accuracy in brain tissue classification.
Outperformed 2D-CNN (96%) and SVM (95%) models in classification accuracy.
Achieved a maximum F1-score of 97.3%, surpassing 2D-CNN and SVM by 2.5% and 11.0%, respectively.

Conclusions:

A 3D-CNN model was developed for brain tissue classification using HSI data.
The 3D-CNN model demonstrates superior performance over conventional 2D-CNN and SVM models.
The proposed model offers higher accuracy for brain tissue classification.