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Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function

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Auxiliary Pneumonia Classification Algorithm Based on Pruning Compression.

Chao-Peng Yang¹, Jian-Qing Zhu¹, Tan Yan²

¹College of Engineering, Huaqiao University, Quanzhou 362021, China.

Computational and Mathematical Methods in Medicine

|July 28, 2022

Summary

This summary is machine-generated.

This study introduces CPGResNet50, a lightweight deep learning model for pneumonia detection using chest X-rays. It significantly reduces model size and computation, enabling efficient clinical application with high accuracy.

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Area of Science:

Medical Imaging
Artificial Intelligence
Computer Science

Background:

Pneumonia is a leading cause of child mortality, with chest X-rays being a common but fallible diagnostic tool.
Deep learning aids pneumonia diagnosis but faces challenges with computational resource limitations in clinical settings.

Purpose of the Study:

To develop a lightweight deep learning model for accurate and efficient pneumonia classification from X-ray images.
To address the limitations of complex deep neural networks in clinical environments.

Main Methods:

Utilized hierarchical channel pruning and optimized convolution (ghost method) to compress the ResNet50 architecture, creating CPGResNet50.
Applied pruning to convolutional layers within the bottleneck of the backbone network.

Main Results:

Reduced ResNet50 parameters by over 85% (from 23.7M to 3.455M) at a 90% pruning rate.
Decreased computational cost (FIOPs) by over 85% and increased processing speed.
Achieved a model training accuracy error within 1%.

Conclusions:

CPGResNet50 offers a highly compressed and efficient solution for auxiliary pneumonia diagnosis.
The method demonstrates strong performance and suitability for clinical application due to its low memory footprint and high precision.