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Related Experiment Video

Updated: Oct 25, 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

A novel deep learning based method for COVID-19 detection from CT image.

SeyyedMohammad JavadiMoghaddam¹, Hossain Gholamalinejad²

¹Department of Computer Engineering, Bozorgmehr University of Qaenat, Qaen, Iran.

Biomedical Signal Processing and Control

|August 4, 2021

Summary

This summary is machine-generated.

A novel deep learning model aids COVID-19 diagnosis using CT scans. This model achieves 99.03% accuracy, offering a fast and effective auxiliary detection tool for the pandemic.

Keywords:

Batch normalization COVID-19 detection method Deep learning model Disease diagnosis Mish function

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Area of Science:

Medical Imaging
Artificial Intelligence
Infectious Diseases

Background:

The COVID-19 pandemic necessitates rapid and accurate diagnostic tools.
Traditional diagnostic kits have limitations, requiring supplementary methods.
Deep learning models show promise in analyzing CT images for COVID-19 detection.

Purpose of the Study:

To propose a novel deep learning model for enhanced COVID-19 diagnosis from CT images.
To optimize the model's convergence time and diagnostic performance.
To evaluate the model's effectiveness against existing deep neural networks.

Main Methods:

A new deep learning architecture incorporating a combined pooling and Squeeze Excitation Block (SE-block) layer.
Utilized Batch Normalization and Mish Function for optimization.
Evaluated the model on a dataset from two public hospitals and compared it with popular deep neural networks (DNNs).

Main Results:

The proposed model achieved a high accuracy of 99.03%.
Demonstrated a rapid recognition time of 0.069 ms in test mode on a GPU.
Outperformed other popular deep neural networks in classification metrics and real-time application suitability.

Conclusions:

The novel deep learning model is highly accurate and efficient for COVID-19 diagnosis using CT scans.
The model's architecture, optimized with SE-block, Batch Normalization, and Mish Function, offers significant advantages.
This approach provides a valuable auxiliary tool for rapid COVID-19 detection during the pandemic.