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Related Experiment Video

Updated: Jul 31, 2025

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SNELM: SqueezeNet-Guided ELM for COVID-19 Recognition.

Yudong Zhang¹, Muhammad Attique Khan², Ziquan Zhu¹

¹School of Computing and Mathematical Sciences, University of Leicester, Leicester, LE1 7RH, UK.

Computer Systems Science and Engineering

|May 8, 2023

Summary

This summary is machine-generated.

A novel SqueezeNet-Extreme Learning Machine (SNELM) model accurately diagnoses COVID-19 using chest CT scans. This AI approach demonstrates high sensitivity and specificity, outperforming existing methods for reliable disease detection.

Keywords:

COVID-19 SqueezeNet complex bypass computed tomography convolutional neural network deep learning extreme learning machine transfer learning

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Published on: June 5, 2021

Area of Science:

Medical Imaging
Artificial Intelligence
Computational Biology

Background:

COVID-19 has caused millions of deaths globally, necessitating accurate diagnostic tools.
Chest computed tomography (CT) offers precise imaging for COVID-19 diagnosis.
Early and accurate diagnosis is crucial for patient management and disease control.

Purpose of the Study:

To develop and evaluate a novel deep learning model for automated COVID-19 diagnosis from chest CT images.
To assess the performance of the SqueezeNet-Extreme Learning Machine (SNELM) model in detecting COVID-19.
To compare the proposed model's diagnostic accuracy against existing state-of-the-art methods.

Main Methods:

Utilized two chest CT datasets (296 and 640 images).
Applied extensive data augmentation techniques to enhance the training dataset.
Developed a SqueezeNet (SN) model with complex bypass for feature extraction.
Employed an Extreme Learning Machine (ELM) classifier with 2000 hidden neurons.
Validated results using 10 runs of 10-fold cross-validation.

Main Results:

The SNELM model achieved high diagnostic performance on both datasets.
For the 296-image dataset: Sensitivity 96.35%, Specificity 96.08%, Precision 96.10%, Accuracy 96.22%.
For the 640-image dataset: Sensitivity 96.00%, Specificity 96.28%, Precision 96.28%, Accuracy 96.14%.
Performance metrics consistently exceeded those of seven other leading COVID-19 recognition models.

Conclusions:

The proposed SNELM model demonstrates significant success in diagnosing COVID-19 from chest CT scans.
The model's high accuracy and robustness suggest its potential as a valuable tool in clinical settings.
SNELM offers a promising AI-driven solution for improving COVID-19 diagnostic capabilities.