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Application of convolution neural network in medical image processing.

Jie Liu¹, Hongbo Zhao²

¹School of Basic Medical Science, Xi'an Medical University, Xi'an, Shaanxi, China.

Technology and Health Care : Official Journal of the European Society for Engineering and Medicine

|January 2, 2021

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This study enhances convolutional neural networks for improved eye disease classification. The novel structure effectively identifies ocular bloodstream diseases from medical images, demonstrating strong classification and robustness.

Area of Science:

Ophthalmology
Computer Science
Medical Imaging

Background:

Convolutional neural networks (CNNs) excel in image classification.
CNNs utilize convolution and sub-sampling layers for feature extraction.
Weight sharing in CNNs significantly reduces training parameters.

Purpose of the Study:

To describe an improved CNN structure for ocular disease recognition.
To introduce a dataset of normal and diseased eye images for analysis.
To utilize MATLAB software for computational implementation.

Main Methods:

The classical LeNet-5 CNN architecture was modified.
A novel CNN structure incorporated varied convolution kernels, sub-sampling techniques, and classifiers.
This improved structure was applied to ocular bloodstream disease recognition.

Keywords:

Convolution neural network back-propagation algorithm image processing self-learning

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Main Results:

The enhanced CNN structure demonstrated high performance on the ocular dataset.
Experimental results confirmed the CNN's strong classification capabilities and robustness.
The improved model accurately classified diseases based on eye bloodstain images.

Conclusions:

The developed convolutional neural network structure is effective for ocular bloodstream disease recognition.
The study highlights the robustness and strong classification performance of CNNs in medical image analysis.
The findings suggest potential for improved diagnostic tools in ophthalmology.