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COVID-19 Disease Prediction Utilizing Dilated Convolution Neural Network Based Levy Flight Tunicate Swarm

Deepak Dahiya¹

¹Computer Science (Tenure Stream), School of Engineering and Computer Science, University of Pittsburgh, Johnstown US.

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|June 26, 2023

Summary

This study introduces a novel deep learning method for early COVID-19 detection using CT scans. The approach enhances diagnostic accuracy, aiding in timely patient care during the pandemic.

Keywords:

Dilated convolution Global pooling Levy flight Neural network Tunicate swarm optimization

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Area of Science:

Medical Imaging
Artificial Intelligence
Computational Biology

Background:

The COVID-19 pandemic has caused widespread health crises globally.
Early diagnosis of COVID-19 is crucial for effective treatment and patient outcomes.
Deep learning offers promising advancements in accelerating diagnostic procedures.

Purpose of the Study:

To propose a novel deep learning-based technique for the early detection of COVID-19.
To enhance the accuracy of COVID-19 diagnosis using medical imaging.

Main Methods:

Application of a Gaussian filter to collected CT images.
Utilizing a tunicate dilated convolutional neural network for image analysis.
Optimizing deep learning hyperparameters with a levy flight-based tunicate behavior algorithm.
Categorizing images as COVID-19 positive or negative.

Main Results:

The proposed deep learning methodology demonstrated superior performance in COVID-19 diagnostic studies.
Evaluation metrics confirmed the effectiveness of the novel approach.
The technique successfully categorized COVID-19 and non-COVID-19 cases with improved accuracy.

Conclusions:

The developed deep learning technique shows significant potential for accurate and early COVID-19 detection.
This approach can aid healthcare professionals in managing the pandemic.
Further research and validation are warranted for clinical implementation.