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Detecting COVID-19 patients via MLES-Net deep learning models from X-Ray images
Wei Wang1, Yongbin Jiang1, Xin Wang1
1School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, 410114, China.
Insights
A new MLES-Net deep learning model accurately diagnoses COVID-19 from X-ray images. This convolutional neural network, MLES-Net56-GAPFC, achieved 100% accuracy for COVID-19 detection, offering a practical solution for timely diagnosis.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Computer Vision
Background:
- Corona Virus Disease 2019 (COVID-19) is a highly infectious pneumonia that emerged in December 2019.
- The rapid global spread and high mortality rate of COVID-19 necessitate accurate and timely diagnostic methods.
- Traditional diagnostic methods face challenges in rapid and widespread application, highlighting the need for advanced solutions.
Purpose of the Study:
- To introduce a novel Multi-Level Enhanced Sensation (MLES) module for improved feature extraction.
- To propose a new convolutional neural network, MLES-Net, designed for enhanced COVID-19 diagnosis.
- To evaluate the effectiveness of MLES-Net in accurately identifying COVID-19 from medical images.
Main Methods:
- The MLES module was developed to automatically focus on key features in medical images.
- Attention mechanisms were employed to generate attention masks by correlating global and local features.
- The MLES-Net model was trained and tested using different classifiers, including FC, GAP, and GAPFC modules.
Main Results:
- The MLES-Net56-GAPFC model achieved an overall accuracy of 95.27%.
- The MLES-Net56-GAPFC model demonstrated a 100% recognition rate specifically for the COVID-19 category.
- The GAPFC classifier provided the best balance of parameters, computation, and detection accuracy.
Conclusions:
- The MLES-Net56-GAPFC model exhibits strong classification capabilities, even with high similarity between COVID-19 categories and low intra-class variability in X-ray images.
- The proposed MLES-Net56-GAPFC offers a practical and efficient solution for COVID-19 diagnosis.
- The model's performance indicates its potential for widespread clinical application in combating the COVID-19 pandemic.
Background:
Corona Virus Disease 2019 (COVID-19) first appeared in December 2019, and spread rapidly around the world. COVID-19 is a pneumonia caused by novel coronavirus infection in 2019. COVID-19 is highly infectious and transmissible. By 7 May 2021, the total number of cumulative number of deaths is 3,259,033. In order to diagnose the infected person in time to prevent the spread of the virus, the diagnosis method for COVID-19 is extremely important. To solve the above problems, this paper introduces a Multi-Level Enhanced Sensation module (MLES), and proposes a new convolutional neural network model, MLES-Net, based on this module.
Methods:
Attention has the ability to automatically focus on the key points in various information, and Attention can realize parallelism, which can replace some recurrent neural networks to a certain extent and improve the efficiency of the model. We used the correlation between global and local features to generate the attention mask. First, the feature map was divided into multiple groups, and the initial attention mask was obtained by the dot product of each feature group and the feature after the global pooling. Then the attention masks were normalized. At the same time, there were two scaling and translating parameters in each group so that the normalize operation could be restored. Then, the final attention mask was obtained through the sigmoid function, and the feature of each location in the original feature group was scaled. Meanwhile, we use different classifiers on the network models with different network layers.
Results:
The network uses three classifiers, FC module (fully connected layer), GAP module (global average pooling layer) and GAPFC module (global average pooling layer and fully connected layer), to improve recognition efficiency. GAPFC as a classifier can obtain the best comprehensive effect by comparing the number of parameters, the amount of calculation and the detection accuracy. The experimental results show that the MLES-Net56-GAPFC achieves the best overall accuracy rate (95.27%) and the best recognition rate for COVID-19 category (100%).
Conclusions:
MLES-Net56-GAPFC has good classification ability for the characteristics of high similarity between categories of COVID-19 X-Ray images and low intra-category variability. Considering the factors such as accuracy rate, number of network model parameters and calculation amount, we believe that the MLES-Net56-GAPFC network model has better practicability.
