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Related Experiment Video

Updated: Oct 19, 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 transfer learning based classification model for covid-19 using chest CT-scans.

Ilyas Lahsaini¹, Mostafa El Habib Daho¹, Mohamed Amine Chikh¹

¹Biomedical Engineering Laboratory, Faculty of Technology, University of Tlemcen, Tlemcen, 13000, Algeria.

Pattern Recognition Letters

|September 27, 2021

Summary

This summary is machine-generated.

This study developed an explainable deep learning model using chest CT scans to detect COVID-19 (Coronavirus Disease 2019). The model aids in diagnosing and monitoring patients, showing promising results for COVID-19 detection.

Keywords:

COVID-19 Deep learning Densenet-121 Densenet-201 Imagenet Transfer learning VGG16 VGG19,Xception

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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Area of Science:

Medical Imaging
Artificial Intelligence
Infectious Diseases

Background:

Coronavirus Disease 2019 (COVID-19) is a significant global health concern.
Accurate and timely diagnosis is crucial for patient management and public health.
Chest CT imaging is a valuable tool for COVID-19 diagnosis.

Purpose of the Study:

To compare the performance of various deep learning models for COVID-19 detection in chest CT images.
To propose an explainable artificial intelligence (XAI) model for COVID-19 detection.
To evaluate the model's effectiveness in diagnosing and monitoring COVID-19 patients.

Main Methods:

Collected a dataset of 4986 RT-PCR confirmed COVID-19 and non-COVID-19 chest CT images from Tlemcen hospital, Algeria.
Applied transfer learning on deep learning models (DenseNet121, DenseNet201, VGG16, VGG19, Inception Resnet-V2, Xception) pre-trained on ImageNet.
Developed an explainable model using DenseNet201 architecture and GradCam for output interpretation.

Main Results:

Comparative analysis of multiple deep learning models was performed.
The proposed explainable model demonstrated promising results for COVID-19 detection.
The GradCam algorithm successfully explained the model's diagnostic decisions.

Conclusions:

The developed explainable deep learning model shows potential for aiding in the diagnosis of COVID-19.
This approach can assist clinicians in managing and monitoring COVID-19 patients.
Explainable AI enhances trust and interpretability in medical diagnostic tools.