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The ensemble deep learning model for novel COVID-19 on CT images
Tao Zhou1,2, Huiling Lu3, Zaoli Yang4
1School of Computer Science and Engineering, North minzu University, Yinchuan 750021, China.
Applied Soft Computing
|November 16, 2020
Summary
This study introduces EDL-COVID, an ensemble deep learning model for rapid COVID-19 detection using CT scans. The model demonstrates superior performance over individual classifiers, aiding in faster diagnosis and containment of the novel coronavirus disease.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Infectious Disease Diagnostics
Background:
- Rapid detection of COVID-19 is crucial for controlling its spread and improving patient outcomes.
- Current diagnostic methods may have limitations in speed and accessibility.
- Deep learning offers potential for automated and efficient analysis of medical images.
Purpose of the Study:
- To develop and evaluate an ensemble deep learning model for the rapid detection of COVID-19 from lung CT images.
- To compare the performance of the proposed ensemble model against individual deep learning classifiers.
- To assess the model's accuracy, sensitivity, specificity, F-value, and Matthews correlation coefficient.
Main Methods:
- Utilized a dataset of 2500 high-quality lung CT images from COVID-19 patients, alongside 2500 images each of lung tumors and normal lungs.
- Employed transfer learning to pre-train three deep convolutional neural network models (AlexNet, GoogleNet, ResNet) for feature extraction.
- Developed an ensemble classifier, EDL-COVID, using relative majority voting with Softmax for classification.
Main Results:
- The EDL-COVID ensemble model outperformed individual component classifiers in overall classification performance.
- Evaluation metrics including accuracy, sensitivity, specificity, F-value, and Matthews correlation coefficient were higher for the ensemble model.
- The proposed algorithm effectively meets the requirements for rapid COVID-19 detection.
Conclusions:
- Ensemble deep learning models can significantly enhance the accuracy and efficiency of COVID-19 detection from CT images.
- The EDL-COVID model presents a promising tool for rapid diagnosis, supporting clinical decision-making and public health efforts.
- Further validation and integration into clinical workflows could accelerate COVID-19 diagnosis and management.
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