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Updated: Nov 12, 2025

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

Published on: December 19, 2020

Transfer learning-based ensemble support vector machine model for automated COVID-19 detection using lung

Mukul Singh¹, Shrey Bansal¹, Sakshi Ahuja²

¹Computer Science and Engineering Department, Indian Institute of Technology Delhi, New Delhi, 110016, India.

Medical & Biological Engineering & Computing

|March 19, 2021

Summary

This summary is machine-generated.

This study introduces an automated COVID-19 screening system using transfer learning and VGG16 for CT scan analysis. The best model achieved 95.7% accuracy, aiding early disease detection.

Keywords:

COVID-19 CT scan data Ensemble SVM Transfer learning VGG16

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Area of Science:

Medical Imaging
Artificial Intelligence
Computational Biology

Background:

COVID-19, caused by SARS-CoV-2, is a global pandemic requiring rapid detection.
Early diagnosis is critical for effective pandemic containment and patient management.
Automated systems can support healthcare professionals, especially in resource-limited settings.

Purpose of the Study:

To propose an automated, transfer learning-based system for COVID-19 screening using CT scans.
To investigate the efficacy of deep learning models, specifically VGG16, for COVID-19 diagnosis.
To compare various classifiers for optimal performance in COVID-19 detection.

Main Methods:

A deep learning model, truncated VGG16, was fine-tuned for feature extraction from CT scans.
Principal Component Analysis (PCA) was employed for feature selection.
Four classifiers (DCNN, ELM, Online Sequential ELM, Bagging Ensemble with SVM) were evaluated.

Main Results:

The Bagging Ensemble with Support Vector Machine (SVM) classifier achieved the highest accuracy (95.7%).
The best model demonstrated high precision (95.8%), AUC (0.958), and F1 score (95.3%) on test data.
The proposed technique showed superiority and robustness across diverse datasets.

Conclusions:

The developed automated system effectively screens COVID-19 using CT scans with high accuracy.
Transfer learning and VGG16 offer a promising approach for rapid and reliable disease diagnosis.
The system can assist medical staff in early COVID-19 detection, particularly during outbreaks.