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PSCNN: PatchShuffle Convolutional Neural Network for COVID-19 Explainable Diagnosis.

Shui-Hua Wang¹, Ziquan Zhu², Yu-Dong Zhang¹

¹School of Computing and Mathematical Sciences, University of Leicester, Leicester, United Kingdom.

Frontiers in Public Health

|November 15, 2021

Summary

This summary is machine-generated.

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This study introduces PSCNN, a novel deep learning model for improved COVID-19 diagnosis. The system achieves high accuracy in identifying lung lesions, outperforming existing methods.

Area of Science:

Medical Imaging
Artificial Intelligence
Computational Biology

Background:

COVID-19 is an infectious disease caused by a novel coronavirus strain.
Accurate and efficient diagnostic systems are crucial for managing the pandemic.
Existing diagnostic methods may have limitations in speed or accuracy.

Purpose of the Study:

To develop a more accurate diagnostic system for COVID-19 using deep learning.
To introduce and evaluate a novel convolutional neural network architecture for disease detection.

Main Methods:

Development of a 12-layer convolutional neural network (12l-CNN) backbone.
Integration of the n-conv module (nCM) and PatchShuffle for regularization.
Utilization of multiple-way data augmentation and Grad-CAM for lesion localization and overfitting prevention.

Keywords:

Grad-CAM PatchShuffle convolutional neural network data augmentation deep learning stochastic pooling

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DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

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Published on: May 10, 2024

Main Results:

The proposed PSCNN model demonstrated high performance across multiple metrics.
Achieved a mean accuracy of 95.53% ± 0.83.
Outperformed 10 state-of-the-art models in COVID-19 diagnosis.

Conclusions:

The PSCNN model offers a superior approach to COVID-19 diagnosis compared to existing methods.
The effectiveness of PatchShuffle as a regularization technique was validated.
The study highlights the potential of deep learning in enhancing infectious disease diagnostics.