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Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

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Detection of COVID-19 and its pulmonary stage using Bayesian hyperparameter optimization and deep feature selection

Nedim Muzoğlu¹, Ahmet Mesrur Halefoğlu², Muhammed Onur Avci¹

¹Department of Biomedical Engineering, Faculty of Engineering Istanbul University-Cerrahpasa Istanbul Turkey.

Expert Systems

|October 17, 2022

Summary

This study introduces an advanced artificial intelligence (AI) approach for accurate COVID-19 detection using computed tomography (CT) scans. The AI model achieved high accuracy in identifying COVID-19 and its lung manifestations.

Keywords:

Bayesian optimization H1N1 viral pneumonia bacterial pneumonia chest CT findings feature selection occlusion sensitivity maps sine–cosine optimization

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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

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Published on: August 16, 2020

Area of Science:

Medical Imaging
Artificial Intelligence
Radiology

Background:

The COVID-19 pandemic necessitated rapid diagnostic tools.
Artificial intelligence (AI) shows promise in supporting healthcare services for disease detection.

Purpose of the Study:

To develop a robust AI approach for detecting COVID-19 and its lung findings from CT images.
To compare the proposed method with existing techniques for improved diagnostic accuracy.

Main Methods:

Utilized pre-trained ShuffleNet, GoogLeNet, and MobileNetV2 models on two distinct datasets.
Employed Sine-Cosine optimization for feature selection and Bayesian optimization for Support Vector Machines (SVM) hyperparameter tuning.
Visualized results using Occlusion Sensitivity Maps and compared with Gradient-weighted Class Activation Mapping (Grad-CAM).

Main Results:

Achieved high overall accuracy: 99.46% on the first dataset (COVID-19, H1N1, bacterial pneumonia, healthy) and 99.82% on the second (lung imaging features).
The proposed AI approach demonstrated superior performance in differentiating COVID-19 from other pneumonias.
Successfully identified various stages of COVID-19 lung involvement.

Conclusions:

The developed AI method offers a powerful and accurate solution for COVID-19 detection from CT scans.
This approach is effective in classifying multiclass viral pneumonia and staging lung involvement in COVID-19 patients.
The study highlights the potential of AI in enhancing diagnostic capabilities for infectious respiratory diseases.