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Updated: Nov 9, 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

Deep Convolutional Neural Network-Based Computer-Aided Detection System for COVID-19 Using Multiple Lung Scans:

Mustafa Ghaderzadeh¹, Farkhondeh Asadi¹, Ramezan Jafari²

¹Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.

Journal of Medical Internet Research

|April 13, 2021

Summary

A deep learning model using NASNet achieved high accuracy in detecting COVID-19 from CT scans. This computer-aided detection system can aid radiologists in early diagnosis, improving healthcare efficiency during the pandemic.

Keywords:

COVID-19 artificial intelligence classification computed tomography scan computer-aided detection convolutional neural network coronavirus deep learning machine learning machine vision model pandemic

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Area of Science:

Medical Imaging
Artificial Intelligence
Computational Biology

Background:

The COVID-19 pandemic strained global healthcare systems, necessitating cost-effective and precise diagnostic tools.
Machine vision and deep learning emerged as critical technologies for early COVID-19 detection.

Purpose of the Study:

To design a highly efficient computer-aided detection (CAD) system for early COVID-19 identification.
To leverage a neural search architecture network (NASNet) algorithm for enhanced diagnostic accuracy.

Main Methods:

Utilized NASNet, a pretrained convolutional neural network, for image feature extraction from CT scans.
Trained and evaluated the model on a local dataset of 10,153 CT scans from 190 COVID-19 positive and 59 negative patients.

Main Results:

The NASNet-based model demonstrated exceptional performance with a detection sensitivity of 0.999, specificity of 0.986, and accuracy of 0.996.
The developed CAD system accurately differentiated all COVID-19 cases from non-COVID-19 cases in the application phase.

Conclusions:

The deep learning-based CAD system shows significant potential for assisting radiologists in early COVID-19 detection.
Implementing this CAD system as a screening tool can accelerate disease diagnosis and conserve valuable healthcare resources.